INN Hotels Project

Context

A significant number of hotel bookings are called-off due to cancellations or no-shows. The typical reasons for cancellations include change of plans, scheduling conflicts, etc. This is often made easier by the option to do so free of charge or preferably at a low cost which is beneficial to hotel guests but it is a less desirable and possibly revenue-diminishing factor for hotels to deal with. Such losses are particularly high on last-minute cancellations.

The new technologies involving online booking channels have dramatically changed customers’ booking possibilities and behavior. This adds a further dimension to the challenge of how hotels handle cancellations, which are no longer limited to traditional booking and guest characteristics.

The cancellation of bookings impact a hotel on various fronts:

• Loss of resources (revenue) when the hotel cannot resell the room.
• Additional costs of distribution channels by increasing commissions or paying for publicity to help sell these rooms.
• Lowering prices last minute, so the hotel can resell a room, resulting in reducing the profit margin.
• Human resources to make arrangements for the guests.

Objective

The increasing number of cancellations calls for a Machine Learning based solution that can help in predicting which booking is likely to be canceled. INN Hotels Group has a chain of hotels in Portugal, they are facing problems with the high number of booking cancellations and have reached out to your firm for data-driven solutions. You as a data scientist have to analyze the data provided to find which factors have a high influence on booking cancellations, build a predictive model that can predict which booking is going to be canceled in advance, and help in formulating profitable policies for cancellations and refunds.

Data Description

The data contains the different attributes of customers' booking details. The detailed data dictionary is given below.

Data Dictionary

• Booking_ID: unique identifier of each booking
• no_of_adults: Number of adults
• no_of_children: Number of Children
• no_of_weekend_nights: Number of weekend nights (Saturday or Sunday) the guest stayed or booked to stay at the hotel
• no_of_week_nights: Number of week nights (Monday to Friday) the guest stayed or booked to stay at the hotel
• type_of_meal_plan: Type of meal plan booked by the customer:
  • Not Selected – No meal plan selected
  • Meal Plan 1 – Breakfast
  • Meal Plan 2 – Half board (breakfast and one other meal)
  • Meal Plan 3 – Full board (breakfast, lunch, and dinner)
• required_car_parking_space: Does the customer require a car parking space? (0 - No, 1- Yes)
• room_type_reserved: Type of room reserved by the customer. The values are ciphered (encoded) by INN Hotels.
• lead_time: Number of days between the date of booking and the arrival date
• arrival_year: Year of arrival date
• arrival_month: Month of arrival date
• arrival_date: Date of the month
• market_segment_type: Market segment designation.
• repeated_guest: Is the customer a repeated guest? (0 - No, 1- Yes)
• no_of_previous_cancellations: Number of previous bookings that were canceled by the customer prior to the current booking
• no_of_previous_bookings_not_canceled: Number of previous bookings not canceled by the customer prior to the current booking
• avg_price_per_room: Average price per day of the reservation; prices of the rooms are dynamic. (in euros)
• no_of_special_requests: Total number of special requests made by the customer (e.g. high floor, view from the room, etc)
• booking_status: Flag indicating if the booking was canceled or not.

Importing necessary libraries and data

# Installing the libraries with the specified version.
#!pip install pandas==1.5.3 numpy==1.25.2 matplotlib==3.7.1 seaborn==0.13.1 scikit-learn==1.2.2 statsmodels==0.14.1 -q --user
import pandas as pd 
import numpy as np 
import seaborn as sns 
import matplotlib.pyplot as plt 
%matplotlib inline 

#select model and sklearn imports 
import statsmodels.api as sm
from sklearn.metrics import roc_curve,recall_score,roc_auc_score,precision_score,accuracy_score,confusion_matrix,f1_score,precision_recall_curve
from sklearn.model_selection import train_test_split,GridSearchCV
from sklearn.tree import DecisionTreeClassifier
from sklearn import tree 

Note: After running the above cell, kindly restart the notebook kernel and run all cells sequentially from the start again.

Loading the dataset

data = pd.read_csv("INNHotelsGroup.csv")
df= data.copy()

Data Overview

• Observations
• Sanity checks

The initial steps to get an overview of any dataset is to:

• observe the first few rows of the dataset, to check whether the dataset has been loaded properly or not get information about the number of rows and columns in the dataset
• find out the data types of the columns to ensure that data is stored in the preferred format and the value of each property is as expected.
• check the statistical summary of the dataset to get an overview of the numerical columns of the data

Displaying the first few rows of the dataset

df.head().T

	0	1	2	3	4
Booking_ID	INN00001	INN00002	INN00003	INN00004	INN00005
no_of_adults	2	2	1	2	2
no_of_children	0	0	0	0	0
no_of_weekend_nights	1	2	2	0	1
no_of_week_nights	2	3	1	2	1
type_of_meal_plan	Meal Plan 1	Not Selected	Meal Plan 1	Meal Plan 1	Not Selected
required_car_parking_space	0	0	0	0	0
room_type_reserved	Room_Type 1	Room_Type 1	Room_Type 1	Room_Type 1	Room_Type 1
lead_time	224	5	1	211	48
arrival_year	2017	2018	2018	2018	2018
arrival_month	10	11	2	5	4
arrival_date	2	6	28	20	11
market_segment_type	Offline	Online	Online	Online	Online
repeated_guest	0	0	0	0	0
no_of_previous_cancellations	0	0	0	0	0
no_of_previous_bookings_not_canceled	0	0	0	0	0
avg_price_per_room	65.0	106.68	60.0	100.0	94.5
no_of_special_requests	0	1	0	0	0
booking_status	Not_Canceled	Not_Canceled	Canceled	Canceled	Canceled

Checking the Shape of the data

df.shape

(36275, 19)

Observation

• The data has 19 columns and 36275 rows

Checking the data types of the columns for the dataset

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 36275 entries, 0 to 36274
Data columns (total 19 columns):
 #   Column                                Non-Null Count  Dtype  
---  ------                                --------------  -----  
 0   Booking_ID                            36275 non-null  object 
 1   no_of_adults                          36275 non-null  int64  
 2   no_of_children                        36275 non-null  int64  
 3   no_of_weekend_nights                  36275 non-null  int64  
 4   no_of_week_nights                     36275 non-null  int64  
 5   type_of_meal_plan                     36275 non-null  object 
 6   required_car_parking_space            36275 non-null  int64  
 7   room_type_reserved                    36275 non-null  object 
 8   lead_time                             36275 non-null  int64  
 9   arrival_year                          36275 non-null  int64  
 10  arrival_month                         36275 non-null  int64  
 11  arrival_date                          36275 non-null  int64  
 12  market_segment_type                   36275 non-null  object 
 13  repeated_guest                        36275 non-null  int64  
 14  no_of_previous_cancellations          36275 non-null  int64  
 15  no_of_previous_bookings_not_canceled  36275 non-null  int64  
 16  avg_price_per_room                    36275 non-null  float64
 17  no_of_special_requests                36275 non-null  int64  
 18  booking_status                        36275 non-null  object 
dtypes: float64(1), int64(13), object(5)
memory usage: 5.3+ MB

Observation

• There are 13 int, 5 objects, 1 float in the data set
• There is no null in the data set
• the target varaible is in object

Statistical summary of the dataset

data.describe()

	no_of_adults	no_of_children	no_of_weekend_nights	no_of_week_nights	required_car_parking_space	lead_time	arrival_year	arrival_month	arrival_date	repeated_guest	no_of_previous_cancellations	no_of_previous_bookings_not_canceled	avg_price_per_room	no_of_special_requests
count	36275.000000	36275.000000	36275.000000	36275.000000	36275.000000	36275.000000	36275.000000	36275.000000	36275.000000	36275.000000	36275.000000	36275.000000	36275.000000	36275.000000
mean	1.844962	0.105279	0.810724	2.204300	0.030986	85.232557	2017.820427	7.423653	15.596995	0.025637	0.023349	0.153411	103.423539	0.619655
std	0.518715	0.402648	0.870644	1.410905	0.173281	85.930817	0.383836	3.069894	8.740447	0.158053	0.368331	1.754171	35.089424	0.786236
min	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	2017.000000	1.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	2.000000	0.000000	0.000000	1.000000	0.000000	17.000000	2018.000000	5.000000	8.000000	0.000000	0.000000	0.000000	80.300000	0.000000
50%	2.000000	0.000000	1.000000	2.000000	0.000000	57.000000	2018.000000	8.000000	16.000000	0.000000	0.000000	0.000000	99.450000	0.000000
75%	2.000000	0.000000	2.000000	3.000000	0.000000	126.000000	2018.000000	10.000000	23.000000	0.000000	0.000000	0.000000	120.000000	1.000000
max	4.000000	10.000000	7.000000	17.000000	1.000000	443.000000	2018.000000	12.000000	31.000000	1.000000	13.000000	58.000000	540.000000	5.000000

Observation

• no_of_adults: Is normally distrubuted.
• no_of_children: The range various from 0 to 10. Which is slightly obnormal
• no_of_weekend_nights,no_of_week_nights,lead_time,no_of_previous_cancellations,no_of_previous_bookings_not_canceled,avg_price_per_room,no_of_special_requests: It looks like the data highly skewed to-words left.

Getting the categorical summary

col=data.select_dtypes(include=np.object_).columns.tolist()
for i,var in enumerate (col):
    print(df[var].value_counts())
    print("***"*15)

Booking_ID
INN00001    1
INN24187    1
INN24181    1
INN24182    1
INN24183    1
           ..
INN12086    1
INN12085    1
INN12084    1
INN12083    1
INN36275    1
Name: count, Length: 36275, dtype: int64
*********************************************
type_of_meal_plan
Meal Plan 1     27835
Not Selected     5130
Meal Plan 2      3305
Meal Plan 3         5
Name: count, dtype: int64
*********************************************
room_type_reserved
Room_Type 1    28130
Room_Type 4     6057
Room_Type 6      966
Room_Type 2      692
Room_Type 5      265
Room_Type 7      158
Room_Type 3        7
Name: count, dtype: int64
*********************************************
market_segment_type
Online           23214
Offline          10528
Corporate         2017
Complementary      391
Aviation           125
Name: count, dtype: int64
*********************************************
booking_status
Not_Canceled    24390
Canceled        11885
Name: count, dtype: int64
*********************************************

Checking the null and duplicate

df.isnull().sum()

Booking_ID                              0
no_of_adults                            0
no_of_children                          0
no_of_weekend_nights                    0
no_of_week_nights                       0
type_of_meal_plan                       0
required_car_parking_space              0
room_type_reserved                      0
lead_time                               0
arrival_year                            0
arrival_month                           0
arrival_date                            0
market_segment_type                     0
repeated_guest                          0
no_of_previous_cancellations            0
no_of_previous_bookings_not_canceled    0
avg_price_per_room                      0
no_of_special_requests                  0
booking_status                          0
dtype: int64

Observation

• The data has no null values

df.duplicated().sum()

0

Observation

• There is no duplicates in the data

Exploratory Data Analysis (EDA)

• EDA is an important part of any project involving data.
• It is important to investigate and understand the data better before building a model with it.
• A few questions have been mentioned below which will help you approach the analysis in the right manner and generate insights from the data.
• A thorough analysis of the data, in addition to the questions mentioned below, should be done.

def histogram_boxplot(data, feature, figsize=(15, 10), kde=False, bins=None):
    """
    Boxplot and histogram combined

    data: dataframe
    feature: dataframe column
    figsize: size of figure (default (15,10))
    kde: whether to show the density curve (default False)
    bins: number of bins for histogram (default None)
    """
    f2, (ax_box2, ax_hist2) = plt.subplots(
        nrows=2,  # Number of rows of the subplot grid= 2
        sharex=True,  # x-axis will be shared among all subplots
        gridspec_kw={"height_ratios": (0.25, 0.75)},
        figsize=figsize,
    )  # creating the 2 subplots
    sns.boxplot(
        data=data, x=feature, ax=ax_box2, showmeans=True, color="violet"
    )  # boxplot will be created and a triangle will indicate the mean value of the column
    sns.histplot(
        data=data, x=feature, kde=kde, ax=ax_hist2, bins=bins
    ) if bins else sns.histplot(
        data=data, x=feature, kde=kde, ax=ax_hist2
    )  # For histogram
    ax_hist2.axvline(
        data[feature].mean(), color="green", linestyle="--"
    )  # Add mean to the histogram
    ax_hist2.axvline(
        data[feature].median(), color="black", linestyle="-"
    )  # Add median to the histogram   

def labeled_barplot(data, feature, perc=False, n=None):
    """
    Barplot with percentage at the top

    data: dataframe
    feature: dataframe column
    perc: whether to display percentages instead of count (default is False)
    n: displays the top n category levels (default is None, i.e., display all levels)
    """

    total = len(data[feature])  # length of the column
    count = data[feature].nunique()
    if n is None:
        plt.figure(figsize=(count + 2, 6))
    else:
        plt.figure(figsize=(n + 2, 6))

    plt.xticks(rotation=90, fontsize=15)
    ax = sns.countplot(
        data=data,
        x=feature,
        palette="Paired",
        order=data[feature].value_counts().index[:n],
    )

    for p in ax.patches:
        if perc == True:
            label = "{:.1f}%".format(
                100 * p.get_height() / total
            )  # percentage of each class of the category
        else:
            label = p.get_height()  # count of each level of the category

        x = p.get_x() + p.get_width() / 2  # width of the plot
        y = p.get_height()  # height of the plot

        ax.annotate(
            label,
            (x, y),
            ha="center",
            va="center",
            size=12,
            xytext=(0, 5),
            textcoords="offset points",
        )  # annotate the percentage

    plt.show()  # show the plot

def distribution_plot_wrt_target(data, predictor, target):

    fig, axs = plt.subplots(2, 2, figsize=(12, 10))

    target_uniq = data[target].unique()

    axs[0, 0].set_title("Distribution of target for target=" + str(target_uniq[0]))
    sns.histplot(
        data=data[data[target] == target_uniq[0]],
        x=predictor,
        kde=True,
        ax=axs[0, 0],
        color="teal",
        stat="density",
    )

    axs[0, 1].set_title("Distribution of target for target=" + str(target_uniq[1]))
    sns.histplot(
        data=data[data[target] == target_uniq[1]],
        x=predictor,
        kde=True,
        ax=axs[0, 1],
        color="orange",
        stat="density",
    )

    axs[1, 0].set_title("Boxplot w.r.t target")
    sns.boxplot(data=data, x=target, y=predictor, ax=axs[1, 0], palette="gist_rainbow")

    axs[1, 1].set_title("Boxplot (without outliers) w.r.t target")
    sns.boxplot(
        data=data,
        x=target,
        y=predictor,
        ax=axs[1, 1],
        showfliers=False,
        palette="gist_rainbow",
    )

    plt.tight_layout()
    plt.show()

Univariate analysis

Histplot and the Boxplot for the adult visiting

histogram_boxplot(df,"no_of_adults",kde=True)

Observation

• The Avg number of the abult are 2. Nearly 25k Visited
• They are couple of the outliers on the both sides of the whiskers
• Max 4 adult visited

Histplot_boxplot for the number of the childern

histogram_boxplot(df,"no_of_children",kde=True)

Observation

• The median is zero, The max is 10 need to check the data

The histplot and the boxplot for no_of_weekend_nights

histogram_boxplot(df,"no_of_weekend_nights",kde=True)

Observation

• The max number of the people stayed are 0 night
• The data showes the there are some people who booked on the weekends stayed for 7 day
• THe max number of the tayed for the 1 and 2 days which is around 20k

The histplot plot and boxplot for the no_of_week_nights

histogram_boxplot(df,"no_of_week_nights",kde=True)

Observation

• The no_of_week_nights the people stayed max 17 days
• The median for no_of_week_nights is 2 days
• The people stayed for 2 days and 3 days are more than the median

Hist and Box plot for required_car_parking_space

histogram_boxplot(df,"required_car_parking_space")

Observation

• They are less people required_car_parking_space comparing the total number of the people

Hist and box plot for lead_time

histogram_boxplot(df,"lead_time",kde=True)

Observation

• Number of days between the date of booking and the arrival date is hihest at 0 days
• The mean range mean range around 90days and the median is around 50days
• The max shows 443 days advance

Hist and box plot arrival_year

histogram_boxplot(df,"arrival_year",kde=True)

Observation

• The max Year of arrival date is 2018

Hist and boxplot arrival_month

histogram_boxplot(df,"arrival_month",kde=True)

Observation

• The max shows octobar but need to check with the year of classification

Hist and box plot for repeated_guest

histogram_boxplot(df,"repeated_guest",kde=True)

Observation

• The data shows repeated_guest are vey less

Hist and Box plot for no_of_previous_cancellations

histogram_boxplot(df,"no_of_previous_cancellations",kde=True)

Observation

• Mojarity of the people are have no_of_previous_cancellations, But the there is one person with 13 times previous_cancellations

Hist and box plot for no_of_previous_bookings_not_canceled

histogram_boxplot(df,"no_of_previous_bookings_not_canceled",kde=True)

Observation

• There are nearly 35k people Number of previous bookings not canceled by the customer prior to the current booking
• no_of_previous_bookings_not_canceled 36 is the max by a person
• the data has more outliers

Hist plot and boxplot for avg_price_per_room

histogram_boxplot(df,"avg_price_per_room",kde=True)

Observation

• The avg avg_price_per_room is nearly 100
• The max price is above 500k. the data is skewed to right
• There is 0 avg_price_per_room needd to check and treat them accordingly

hist and box plot for no_of_special_requests

histogram_boxplot(df,"no_of_special_requests",kde=True)

Observation

• Majority of customers no_of_special_requests
• max no_of_special_requests are 5

categorical plots

labeled_barplot(df,"no_of_special_requests",perc=True)

C:\Users\Miguel\AppData\Local\Temp\ipykernel_2840\3025790399.py:19: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  ax = sns.countplot(

Observation

• There is 54.5% of no request and remaing 46.5% have the requests

labeled_barplot(df,"no_of_week_nights",perc=True)

C:\Users\Miguel\AppData\Local\Temp\ipykernel_2840\3025790399.py:19: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  ax = sns.countplot(

cout plot for type_of_meal_plan

labeled_barplot(df,"type_of_meal_plan",perc=True)

C:\Users\Miguel\AppData\Local\Temp\ipykernel_2840\3025790399.py:19: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  ax = sns.countplot(

Observation

• Majority of the customers opted the meal plan 1
• Zero perc are opted for the Meal plan 3

cout plot for room_type_reserved

labeled_barplot(df,"room_type_reserved",perc=True)

C:\Users\Miguel\AppData\Local\Temp\ipykernel_2840\3025790399.py:19: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  ax = sns.countplot(

Observation

• Majority of the people are opted for the Room_type1, followed by Room_type_4
• Room_type are ciphered by the company

cout plot for market_segment_type

Which market segment do most of the guests come from?

labeled_barplot(df,"market_segment_type",perc=True)

C:\Users\Miguel\AppData\Local\Temp\ipykernel_2840\3025790399.py:19: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  ax = sns.countplot(

Observation

• The online market_segment done great job and followed by the offline
• Aviation is the least among all

cout plot for booking_status

What percentage of bookings are canceled?

labeled_barplot(df,"booking_status",perc=True)

C:\Users\Miguel\AppData\Local\Temp\ipykernel_2840\3025790399.py:19: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  ax = sns.countplot(

Observation

• Majority booking_status are not cancelled 67.2%
• canceled is 32.8%

What are the busiest months in the hotel?

plt.figure(figsize=(10,8))
sns.histplot(df,x="arrival_month",hue="arrival_year");

df.groupby(["arrival_month"])["arrival_year"].value_counts()

arrival_month  arrival_year
1              2018            1014
2              2018            1704
3              2018            2358
4              2018            2736
5              2018            2598
6              2018            3203
7              2018            2557
               2017             363
8              2018            2799
               2017            1014
9              2018            2962
               2017            1649
10             2018            3404
               2017            1913
11             2018            2333
               2017             647
12             2018            2093
               2017             928
Name: count, dtype: int64

Observation

• The individually the 2018 OCTOBER Busiest month with 3404, followed by 2018 SEPTEMBER with 2962

Hotel rates are dynamic and change according to demand and customer demographics. What are the differences in room prices in different market segments?

plt.figure(figsize=(10,8))
sns.histplot(df,x="avg_price_per_room",hue="market_segment_type");

df.groupby(["market_segment_type"])["avg_price_per_room"].sum()

market_segment_type
Aviation           12588.00
Complementary       1228.43
Corporate         167232.98
Offline           964708.84
Online           2605930.63
Name: avg_price_per_room, dtype: float64

Observation

• Online avg_price_per_room is highest comparing remaing market_segment_type

Repeating guests are the guests who stay in the hotel often and are important to brand equity. What percentage of repeating guests cancel?

labeled_barplot(df,"repeated_guest",perc=True)

C:\Users\Miguel\AppData\Local\Temp\ipykernel_2840\3025790399.py:19: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  ax = sns.countplot(

Observation

• The 2.6% of repeated_guest had cancelled

Many guests have special requirements when booking a hotel room. Do these requirements affect booking cancellation?

distribution_plot_wrt_target(df,"no_of_special_requests","booking_status")

C:\Users\Miguel\AppData\Local\Temp\ipykernel_2840\2989500541.py:28: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.boxplot(data=data, x=target, y=predictor, ax=axs[1, 0], palette="gist_rainbow")
C:\Users\Miguel\AppData\Local\Temp\ipykernel_2840\2989500541.py:31: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.boxplot(

df.groupby(["booking_status"])["no_of_special_requests"].value_counts()

booking_status  no_of_special_requests
Canceled        0                          8545
                1                          2703
                2                           637
Not_Canceled    0                         11232
                1                          8670
                2                          3727
                3                           675
                4                            78
                5                             8
Name: count, dtype: int64

Observation

• there no specific reason for the cancelation it shows 8545 has no special request
• 3341 has 1 and 2 specical request so they have cancelled

distribution_plot_wrt_target(df,"no_of_weekend_nights","booking_status")

C:\Users\Miguel\AppData\Local\Temp\ipykernel_2840\2989500541.py:28: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.boxplot(data=data, x=target, y=predictor, ax=axs[1, 0], palette="gist_rainbow")
C:\Users\Miguel\AppData\Local\Temp\ipykernel_2840\2989500541.py:31: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.boxplot(

Observation

• There are many cancelation on no_of_weekend_nights compared to non cancelled

distribution_plot_wrt_target(df,"no_of_week_nights","booking_status")

C:\Users\Miguel\AppData\Local\Temp\ipykernel_2840\2989500541.py:28: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.boxplot(data=data, x=target, y=predictor, ax=axs[1, 0], palette="gist_rainbow")
C:\Users\Miguel\AppData\Local\Temp\ipykernel_2840\2989500541.py:31: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.boxplot(

Observation

• There is equal amount of canceled and the non-canceled no_of_week_nights

distribution_plot_wrt_target(df,"avg_price_per_room","booking_status")

C:\Users\Miguel\AppData\Local\Temp\ipykernel_2840\2989500541.py:28: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.boxplot(data=data, x=target, y=predictor, ax=axs[1, 0], palette="gist_rainbow")
C:\Users\Miguel\AppData\Local\Temp\ipykernel_2840\2989500541.py:31: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.boxplot(

Observation

• Need to know why there is 0 in the avg_price, even there is 0 dollars there are some cancelation. should assume there was a promotional campaign held previosly
• There is almost the same on the booking status

distribution_plot_wrt_target(df,"avg_price_per_room","arrival_year")

C:\Users\Miguel\AppData\Local\Temp\ipykernel_2840\2989500541.py:28: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.boxplot(data=data, x=target, y=predictor, ax=axs[1, 0], palette="gist_rainbow")
C:\Users\Miguel\AppData\Local\Temp\ipykernel_2840\2989500541.py:31: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.boxplot(

Observation

• There are Zerodollar on the both the years need to find in which month there were noted
• 2018 there was move sale and more money generated compared to previos year

sns.barplot(df,x="arrival_month",y="avg_price_per_room",hue="arrival_year");

Observation

• It shows there is Zero dollars in all the month
• the company started from the 7 month of 2017

plt.xticks(rotation=90)
sns.barplot(df,x="avg_price_per_room",y="market_segment_type");

Observation

• The promotional campaign was held on each month with all type of the rooms

labeled_barplot(df,"avg_price_per_room",perc=True,n=10)

C:\Users\Miguel\AppData\Local\Temp\ipykernel_2840\3025790399.py:19: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  ax = sns.countplot(

Observation

• 1.5 of the avg_price of a room is zero dollars
• which will play significtent roll in the data which will cause outliers on the lower side of the whickers

plt.figure(figsize=(10,9))
sns.heatmap(df.corr(numeric_only=True),annot=True,cbar=True)

<Axes: >

• There is no much information obtained from the heat map
• There is good corelation b/w repeated_guest and no_of_previous_bookings_not_canceled
• There is significent relation b/w no_of_previous_bookings_not_canceled and no_of_previous_cancellations

Data Preprocessing

• Missing value treatment (if needed)
• Feature engineering (if needed)
• Outlier detection and treatment (if needed)
• Preparing data for modeling
• Any other preprocessing steps (if needed)

- Missing value treatment (if needed)

df.isnull().sum()

Booking_ID                              0
no_of_adults                            0
no_of_children                          0
no_of_weekend_nights                    0
no_of_week_nights                       0
type_of_meal_plan                       0
required_car_parking_space              0
room_type_reserved                      0
lead_time                               0
arrival_year                            0
arrival_month                           0
arrival_date                            0
market_segment_type                     0
repeated_guest                          0
no_of_previous_cancellations            0
no_of_previous_bookings_not_canceled    0
avg_price_per_room                      0
no_of_special_requests                  0
booking_status                          0
dtype: int64

Observation

• There are no nulls in the data

- Feature engineering

• We found out there are 1.5% of zeros in the avg_cost we need to check the remaing outliers and treat them

numerical_col = data.select_dtypes(include=np.number).columns.tolist()
plt.figure(figsize=(20, 30))

for i, variable in enumerate(numerical_col):
    plt.subplot(5, 4, i + 1)
    plt.boxplot(data[variable], whis=1.5)
    plt.tight_layout()
    plt.title(variable)

plt.show()

• no_of_week_night,lead_time, no_ofprevious_cancelation,no_of_previous_booking_not cancelled and n0_of_special_requests have upper outliers.
• avg_cost_per_room has both upper and lower outliers
• arrival_date has no outliers

Treating the outliers for Avg_price_per_room

Q1 = df["avg_price_per_room"].quantile(0.25)  # 25th quantile
Q3 = df["avg_price_per_room"].quantile(0.75)  # 75th quantile
IQR = Q3 - Q1
Lower_Whisker = Q1 - 1.5 * IQR
Upper_Whisker = Q3 + 1.5 * IQR
df["avg_price_per_room"] = np.clip(df["avg_price_per_room"], Lower_Whisker, Upper_Whisker)
df["avg_price_per_room"].value_counts()

avg_price_per_room
179.55    1069
65.00      848
75.00      826
90.00      703
95.00      669
          ... 
102.86       1
143.99       1
90.44        1
120.91       1
167.80       1
Name: count, Length: 3513, dtype: int64

numerical_col = df.select_dtypes(include=np.number).columns.tolist()
plt.figure(figsize=(20, 30))

for i, variable in enumerate(numerical_col):
    plt.subplot(5, 4, i + 1)
    plt.boxplot(data[variable], whis=1.5)
    plt.tight_layout()
    plt.title(variable)

plt.show()

Preparing data for modeling

we are dropping Booking_ids

df=df.drop("Booking_ID",axis=1)
data=df.copy()

We are reshaping the type_of_meal_plan, arrival_year, and booking_status

• Remaing we can use the dummpy

df["booking_status"].unique()

array(['Not_Canceled', 'Canceled'], dtype=object)

reshape={
    "type_of_meal_plan":{"Not Selected":0,"Meal Plan 3":1,"Meal Plan 2":2,"Meal Plan 1":3},
    "arrival_year":{2017:0,2018:1},
    "booking_status":{"Not_Canceled":1,"Canceled":0}
}
df=df.replace(reshape)

Preparing data for modeling

• what model we are doing?
• Logit
• Decisiontree
• gvsearch
• ccp (cost- complex pruning)

X= df.drop("booking_status",axis=1)
Y =df["booking_status"]

X=pd.get_dummies(X,drop_first=1,dtype=float)

X

	no_of_adults	no_of_children	no_of_weekend_nights	no_of_week_nights	type_of_meal_plan	required_car_parking_space	lead_time	arrival_year	arrival_month	arrival_date	...	room_type_reserved_Room_Type 2	room_type_reserved_Room_Type 3	room_type_reserved_Room_Type 4	room_type_reserved_Room_Type 5	room_type_reserved_Room_Type 6	room_type_reserved_Room_Type 7	market_segment_type_Complementary	market_segment_type_Corporate	market_segment_type_Offline	market_segment_type_Online
0	2	0	1	2	3	0	224	0	10	2	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0
1	2	0	2	3	0	0	5	1	11	6	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0
2	1	0	2	1	3	0	1	1	2	28	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0
3	2	0	0	2	3	0	211	1	5	20	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0
4	2	0	1	1	0	0	48	1	4	11	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
36270	3	0	2	6	3	0	85	1	8	3	...	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0
36271	2	0	1	3	3	0	228	1	10	17	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0
36272	2	0	2	6	3	0	148	1	7	1	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0
36273	2	0	0	3	0	0	63	1	4	21	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0
36274	2	0	1	2	3	0	207	1	12	30	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0

36275 rows × 25 columns

#adding the constant to X
X=sm.add_constant(X)
x_train, x_test, y_train, y_test = train_test_split(
    X, Y, test_size=0.25, random_state=1
)
y_train

8271     1
34593    0
18637    1
34925    1
8385     1
        ..
7813     0
32511    0
5192     0
12172    1
33003    0
Name: booking_status, Length: 27206, dtype: int64

print("Shape of Training set : ", x_train.shape)
print("Shape of test set : ", x_test.shape)
print("Percentage of classes in training set:")
print(y_train.value_counts(normalize=True))
print("Percentage of classes in test set:")
print(y_test.value_counts(normalize=True))

Shape of Training set :  (27206, 26)
Shape of test set :  (9069, 26)
Percentage of classes in training set:
booking_status
1    0.671874
0    0.328126
Name: proportion, dtype: float64
Percentage of classes in test set:
booking_status
1    0.673834
0    0.326166
Name: proportion, dtype: float64

Model Building - Logistic Regression

• We will now perform logistic regression using statsmodels, a Python module that provides functions for the estimation of many statistical models, as well as for conducting statistical tests, and statistical data exploration.

• Using statsmodels, we will be able to check the statistical validity of our model - identify the significant predictors from p-values that we get for each predictor variable.

logit =sm.Logit(y_train,x_train.astype(float)).fit()
logit.summary()

Warning: Maximum number of iterations has been exceeded.
         Current function value: 0.424620
         Iterations: 35

C:\Users\Miguel\AppData\Roaming\Python\Python310\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "

Logit Regression Results

Dep. Variable:	booking_status	No. Observations:	27206
Model:	Logit	Df Residuals:	27180
Method:	MLE	Df Model:	25
Date:	Sat, 29 Jun 2024	Pseudo R-squ.:	0.3290
Time:	18:45:09	Log-Likelihood:	-11552.
converged:	False	LL-Null:	-17217.
Covariance Type:	nonrobust	LLR p-value:	0.000

	coef	std err	z	P>|z|	[0.025	0.975]
const	2.7856	0.268	10.401	0.000	2.261	3.311
no_of_adults	-0.1052	0.036	-2.892	0.004	-0.176	-0.034
no_of_children	-0.1576	0.055	-2.854	0.004	-0.266	-0.049
no_of_weekend_nights	-0.1188	0.019	-6.220	0.000	-0.156	-0.081
no_of_week_nights	-0.0421	0.012	-3.552	0.000	-0.065	-0.019
type_of_meal_plan	0.1025	0.016	6.229	0.000	0.070	0.135
required_car_parking_space	1.5966	0.133	11.964	0.000	1.335	1.858
lead_time	-0.0157	0.000	-62.242	0.000	-0.016	-0.015
arrival_year	-0.4371	0.056	-7.869	0.000	-0.546	-0.328
arrival_month	0.0429	0.006	6.907	0.000	0.031	0.055
arrival_date	-0.0014	0.002	-0.742	0.458	-0.005	0.002
repeated_guest	2.1004	0.568	3.701	0.000	0.988	3.213
no_of_previous_cancellations	-0.2299	0.078	-2.958	0.003	-0.382	-0.078
no_of_previous_bookings_not_canceled	0.1857	0.158	1.174	0.240	-0.124	0.496
avg_price_per_room	-0.0205	0.001	-28.209	0.000	-0.022	-0.019
no_of_special_requests	1.4784	0.029	50.775	0.000	1.421	1.535
room_type_reserved_Room_Type 2	0.3624	0.126	2.875	0.004	0.115	0.609
room_type_reserved_Room_Type 3	0.0063	1.303	0.005	0.996	-2.548	2.561
room_type_reserved_Room_Type 4	0.3036	0.052	5.871	0.000	0.202	0.405
room_type_reserved_Room_Type 5	0.7278	0.198	3.670	0.000	0.339	1.116
room_type_reserved_Room_Type 6	0.7776	0.139	5.602	0.000	0.506	1.050
room_type_reserved_Room_Type 7	0.8632	0.269	3.210	0.001	0.336	1.390
market_segment_type_Complementary	25.7582	4.55e+04	0.001	1.000	-8.92e+04	8.92e+04
market_segment_type_Corporate	1.1393	0.260	4.385	0.000	0.630	1.649
market_segment_type_Offline	2.1182	0.249	8.502	0.000	1.630	2.606
market_segment_type_Online	0.3738	0.246	1.520	0.128	-0.108	0.856

Observation

• Negative values of the coefficient show that the probability of a customer cancelation decreases with the increase of the corresponding attribute value.
• Positive values of the coefficient show that the probability of a customer non-cancelation increases with the increase of the corresponding attribute value.

Model Performance Evaluation

Model can make wrong predictions as:

1. Predicting a customer has cancel but in reality the customer has not cancelled.

2. Predicting a customer doesn't cancel but in reality the salary of the person canceled.

Which case is more important?

• Both the cases are important as:

  • If we Predicting a customer has cancel but in reality the customer has not cancelled then hostel need to accommodate the person or hotel will loss the customer.

  • If we Predicting a customer doesn't cancel but in reality the customer canceled then hotel will loss the money.

How to reduce this loss?

• We need to reduce both False Negatives and False Positives

• f1_score should be maximized as the greater the f1_score, the higher the chances of reducing both False Negatives and False Positives and identifying both the classes correctly

  • fi_score is computed as $$f1\_score = \frac{2 * Precision * Recall}{Precision + Recall}$$

• First, let's create functions to calculate different metrics and confusion matrix so that we don't have to use the same code repeatedly for each model.

• The model_performance_classification_statsmodels function will be used to check the model performance of models.

• The confusion_matrix_statsmodels function will be used to plot confusion matri

# defining a function to compute different metrics to check performance of a classification model built using statsmodels
def model_performance_classification_statsmodels(
    model, predictors, target, threshold=0.5
):
    """
    Function to compute different metrics to check classification model performance

    model: classifier
    predictors: independent variables
    target: dependent variable
    threshold: threshold for classifying the observation as class 1
    """

    # checking which probabilities are greater than threshold
    pred_temp = model.predict(predictors) > threshold
    # rounding off the above values to get classes
    pred = np.round(pred_temp)

    acc = accuracy_score(target, pred)  # to compute Accuracy
    recall = recall_score(target, pred)  # to compute Recall
    precision = precision_score(target, pred)  # to compute Precision
    f1 = f1_score(target, pred)  # to compute F1-score

    # creating a dataframe of metrics
    df_perf = pd.DataFrame(
        {"Accuracy": acc, "Recall": recall, "Precision": precision, "F1": f1,},
        index=[0],
    )

    return df_perf

# defining a function to plot the confusion_matrix of a classification model


def confusion_matrix_statsmodels(model, predictors, target, threshold=0.5):
    """
    To plot the confusion_matrix with percentages

    model: classifier
    predictors: independent variables
    target: dependent variable
    threshold: threshold for classifying the observation as class 1
    """
    y_pred = model.predict(predictors) > threshold
    cm = confusion_matrix(target, y_pred)
    labels = np.asarray(
        [
            ["{0:0.0f}".format(item) + "\n{0:.2%}".format(item / cm.flatten().sum())]
            for item in cm.flatten()
        ]
    ).reshape(2, 2)

    plt.figure(figsize=(6, 4))
    sns.heatmap(cm, annot=labels, fmt="")
    plt.ylabel("True label")
    plt.xlabel("Predicted label")

confusion_matrix_statsmodels(logit, x_train, y_train)

model_performance_classification_statsmodels(logit, x_train, y_train)

	Accuracy	Recall	Precision	F1
0	0.805852	0.889874	0.832659	0.860316

Checking Multicollinearity

• In order to make statistical inferences from a logistic regression model, it is important to ensure that there is no multicollinearity present in the data.

Detecting and Dealing with Multicollinearity

• There are different ways of detecting (or testing for) multicollinearity. One such way is using the Variation Inflation Factor (VIF).

• Variance Inflation factor: Variance inflation factors measure the inflation in the variances of the regression coefficients estimates due to collinearities that exist among the predictors. It is a measure of how much the variance of the estimated regression coefficient $\beta_k$ is "inflated" by the existence of correlation among the predictor variables in the model.

• General Rule of thumb:

  • If VIF is 1 then there is no correlation among the $k$th predictor and the remaining predictor variables, and hence the variance of $\beta_k$ is not inflated at all
  • If VIF exceeds 5, we say there is moderate multicollinearity
  • If VIF is equal or exceeding 10, it shows signs of high multi-collinearity

• The purpose of the analysis should dictate which threshold to use

from statsmodels.stats.outliers_influence import variance_inflation_factor
# Calculate VIF for each feature
vif_data = {
    'Feature': x_train.columns,
    'VIF': [variance_inflation_factor(x_train.values, i) for i in range(x_train.shape[1])]
}

# Create a DataFrame with the VIF values
vif_df = pd.DataFrame(vif_data)

# Sort the DataFrame by VIF values in descending order
vif_df_sorted = vif_df.sort_values(by='VIF', ascending=False).reset_index(drop=True)

# Display the sorted DataFrame
print("VIF values sorted in descending order:\n")
print(vif_df_sorted)

VIF values sorted in descending order:

                                 Feature         VIF
0                                  const  352.301359
1             market_segment_type_Online   72.020663
2            market_segment_type_Offline   64.915167
3          market_segment_type_Corporate   17.163202
4      market_segment_type_Complementary    4.406718
5                         no_of_children    1.989761
6         room_type_reserved_Room_Type 6    1.943554
7                     avg_price_per_room    1.787892
8                         repeated_guest    1.783108
9   no_of_previous_bookings_not_canceled    1.612068
10        room_type_reserved_Room_Type 4    1.369397
11          no_of_previous_cancellations    1.364936
12                          no_of_adults    1.346828
13                          arrival_year    1.331140
14                             lead_time    1.291926
15                         arrival_month    1.253170
16                no_of_special_requests    1.248142
17                     type_of_meal_plan    1.190052
18        room_type_reserved_Room_Type 2    1.102830
19                     no_of_week_nights    1.094269
20        room_type_reserved_Room_Type 7    1.078913
21                  no_of_weekend_nights    1.069453
22            required_car_parking_space    1.039371
23        room_type_reserved_Room_Type 5    1.029612
24                          arrival_date    1.005960
25        room_type_reserved_Room_Type 3    1.002985

Observation

• market_segment_type_Online, market_segment_type_Offline and market_segment_type_Corporate are from categorical from market_segment_type

• We will drop market_segment_type_Online as we get the same information from market_segment_type_Offline

x_train1 = x_train.drop("market_segment_type_Online", axis=1)
x_test1=x_test.drop("market_segment_type_Online",axis=1)
vif_series2 = pd.Series(
    [variance_inflation_factor(x_train1.values, i) for i in range(x_train1.shape[1])],
    index=x_train1.columns,
)
print("Series before feature selection: \n\n{}\n".format(vif_series2))

Series before feature selection: 

const                                   53.486802
no_of_adults                             1.330410
no_of_children                           1.988884
no_of_weekend_nights                     1.069006
no_of_week_nights                        1.093586
type_of_meal_plan                        1.188876
required_car_parking_space               1.039276
lead_time                                1.288635
arrival_year                             1.329425
arrival_month                            1.252329
arrival_date                             1.005951
repeated_guest                           1.779453
no_of_previous_cancellations             1.364618
no_of_previous_bookings_not_canceled     1.611857
avg_price_per_room                       1.787542
no_of_special_requests                   1.243364
room_type_reserved_Room_Type 2           1.102668
room_type_reserved_Room_Type 3           1.002984
room_type_reserved_Room_Type 4           1.363842
room_type_reserved_Room_Type 5           1.029611
room_type_reserved_Room_Type 6           1.943293
room_type_reserved_Room_Type 7           1.078832
market_segment_type_Complementary        1.253555
market_segment_type_Corporate            1.523028
market_segment_type_Offline              1.485010
dtype: float64

Dropping market_segment_type_Online fixes the multicollinearity in market_segment_type column.

Building a Logistic Regression model

logit_1=sm.Logit(y_train,x_train1.astype(float)).fit()
logit_1.summary()

Warning: Maximum number of iterations has been exceeded.
         Current function value: 0.424661
         Iterations: 35

C:\Users\Miguel\AppData\Roaming\Python\Python310\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "

Logit Regression Results

Dep. Variable:	booking_status	No. Observations:	27206
Model:	Logit	Df Residuals:	27181
Method:	MLE	Df Model:	24
Date:	Sat, 29 Jun 2024	Pseudo R-squ.:	0.3290
Time:	18:45:12	Log-Likelihood:	-11553.
converged:	False	LL-Null:	-17217.
Covariance Type:	nonrobust	LLR p-value:	0.000

	coef	std err	z	P>|z|	[0.025	0.975]
const	3.1477	0.124	25.485	0.000	2.906	3.390
no_of_adults	-0.0983	0.036	-2.726	0.006	-0.169	-0.028
no_of_children	-0.1557	0.055	-2.818	0.005	-0.264	-0.047
no_of_weekend_nights	-0.1196	0.019	-6.262	0.000	-0.157	-0.082
no_of_week_nights	-0.0428	0.012	-3.611	0.000	-0.066	-0.020
type_of_meal_plan	0.1016	0.016	6.179	0.000	0.069	0.134
required_car_parking_space	1.5941	0.133	11.944	0.000	1.333	1.856
lead_time	-0.0156	0.000	-62.305	0.000	-0.016	-0.015
arrival_year	-0.4401	0.056	-7.929	0.000	-0.549	-0.331
arrival_month	0.0426	0.006	6.857	0.000	0.030	0.055
arrival_date	-0.0014	0.002	-0.745	0.456	-0.005	0.002
repeated_guest	2.0684	0.568	3.643	0.000	0.955	3.181
no_of_previous_cancellations	-0.2268	0.078	-2.919	0.004	-0.379	-0.075
no_of_previous_bookings_not_canceled	0.1853	0.157	1.179	0.238	-0.123	0.493
avg_price_per_room	-0.0205	0.001	-28.186	0.000	-0.022	-0.019
no_of_special_requests	1.4801	0.029	50.870	0.000	1.423	1.537
room_type_reserved_Room_Type 2	0.3651	0.126	2.897	0.004	0.118	0.612
room_type_reserved_Room_Type 3	0.0073	1.303	0.006	0.996	-2.547	2.561
room_type_reserved_Room_Type 4	0.2983	0.052	5.781	0.000	0.197	0.399
room_type_reserved_Room_Type 5	0.7287	0.198	3.675	0.000	0.340	1.117
room_type_reserved_Room_Type 6	0.7739	0.139	5.576	0.000	0.502	1.046
room_type_reserved_Room_Type 7	0.8583	0.269	3.192	0.001	0.331	1.385
market_segment_type_Complementary	25.4889	4.78e+04	0.001	1.000	-9.36e+04	9.37e+04
market_segment_type_Corporate	0.7735	0.099	7.824	0.000	0.580	0.967
market_segment_type_Offline	1.7465	0.048	36.208	0.000	1.652	1.841

print("Training Performance")
model_performance_classification_statsmodels(logit_1, x_train1, y_train)

Training Performance

	Accuracy	Recall	Precision	F1
0	0.806146	0.890038	0.832898	0.86052

Removing high p-value variables

• For other attributes present in the data, the p-values are high only for few dummy variables and since only one (or some) of the categorical levels have a high p-value we will drop them iteratively as sometimes p-values change after dropping a variable. So, we'll not drop all variables at once.

• Instead, we will do the following repeatedly using a loop:

  • Build a model, check the p-values of the variables, and drop the column with the highest p-value.
  • Create a new model without the dropped feature, check the p-values of the variables, and drop the column with the highest p-value.
  • Repeat the above two steps till there are no columns with p-value > 0.05.

Note: The above process can also be done manually by picking one variable at a time that has a high p-value, dropping it, and building a model again. But that might be a little tedious and using a loop will be more efficien

# initial list of columns
cols = x_train1.columns.tolist()

# setting an initial max p-value
max_p_value = 1

while len(cols) > 0:
    # defining the train set
    x_train_aux = x_train1[cols]

    # fitting the model
    model = sm.Logit(y_train, x_train_aux).fit(disp=False)

    # getting the p-values and the maximum p-value
    p_values = model.pvalues
    max_p_value = max(p_values)

    # name of the variable with maximum p-value
    feature_with_p_max = p_values.idxmax()

    if max_p_value > 0.05:
        cols.remove(feature_with_p_max)
    else:
        break

selected_features = cols
print(selected_features)

C:\Users\Miguel\AppData\Roaming\Python\Python310\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "

['const', 'no_of_adults', 'no_of_children', 'no_of_weekend_nights', 'no_of_week_nights', 'type_of_meal_plan', 'required_car_parking_space', 'lead_time', 'arrival_year', 'arrival_month', 'repeated_guest', 'no_of_previous_cancellations', 'avg_price_per_room', 'no_of_special_requests', 'room_type_reserved_Room_Type 2', 'room_type_reserved_Room_Type 4', 'room_type_reserved_Room_Type 5', 'room_type_reserved_Room_Type 6', 'room_type_reserved_Room_Type 7', 'market_segment_type_Corporate', 'market_segment_type_Offline']

x_test2=x_test1[selected_features]
x_train2=x_train1[selected_features]
logit_2=sm.Logit(y_train,x_train2.astype(float)).fit()
logit_2.summary()

Optimization terminated successfully.
         Current function value: 0.425266
         Iterations 9

Logit Regression Results

Dep. Variable:	booking_status	No. Observations:	27206
Model:	Logit	Df Residuals:	27185
Method:	MLE	Df Model:	20
Date:	Sat, 29 Jun 2024	Pseudo R-squ.:	0.3280
Time:	18:45:13	Log-Likelihood:	-11570.
converged:	True	LL-Null:	-17217.
Covariance Type:	nonrobust	LLR p-value:	0.000

	coef	std err	z	P>|z|	[0.025	0.975]
const	3.1684	0.119	26.530	0.000	2.934	3.402
no_of_adults	-0.1019	0.036	-2.828	0.005	-0.173	-0.031
no_of_children	-0.1547	0.055	-2.803	0.005	-0.263	-0.047
no_of_weekend_nights	-0.1213	0.019	-6.355	0.000	-0.159	-0.084
no_of_week_nights	-0.0442	0.012	-3.735	0.000	-0.067	-0.021
type_of_meal_plan	0.1050	0.016	6.390	0.000	0.073	0.137
required_car_parking_space	1.5967	0.134	11.960	0.000	1.335	1.858
lead_time	-0.0157	0.000	-62.550	0.000	-0.016	-0.015
arrival_year	-0.4379	0.055	-7.894	0.000	-0.547	-0.329
arrival_month	0.0438	0.006	7.070	0.000	0.032	0.056
repeated_guest	2.5182	0.504	4.992	0.000	1.530	3.507
no_of_previous_cancellations	-0.2022	0.072	-2.794	0.005	-0.344	-0.060
avg_price_per_room	-0.0209	0.001	-29.071	0.000	-0.022	-0.019
no_of_special_requests	1.4790	0.029	50.865	0.000	1.422	1.536
room_type_reserved_Room_Type 2	0.3561	0.126	2.828	0.005	0.109	0.603
room_type_reserved_Room_Type 4	0.3033	0.052	5.880	0.000	0.202	0.404
room_type_reserved_Room_Type 5	0.7434	0.198	3.764	0.000	0.356	1.131
room_type_reserved_Room_Type 6	0.7898	0.139	5.694	0.000	0.518	1.062
room_type_reserved_Room_Type 7	0.8837	0.268	3.297	0.001	0.358	1.409
market_segment_type_Corporate	0.7602	0.099	7.698	0.000	0.567	0.954
market_segment_type_Offline	1.7374	0.048	36.050	0.000	1.643	1.832

Coefficient Interpretations

• Coefficient of some levels of required_car_parking_space, repeated_guest,no_of_special_requests and market_segment_type_Offline are positive an increase in these will lead to increase in chances of a customer not-Cancel.

• Coefficient of lead_time, arrival_month, no_of_week_nights, no_of_weekend_nights, no_of_children, no_of_adults, and no_of_previous_cancellations are negative increase in these will lead to cancelation.

Converting coefficients to odds

• The coefficients ($\beta$s) of the logistic regression model are in terms of $log(odds)$ and to find the odds, we have to take the exponential of the coefficients
• Therefore, $odds = exp(\beta)$
• The percentage change in odds is given as $(exp(\beta) - 1) * 100$

# converting coefficients to odds
odds = np.exp(logit_2.params)

# finding the percentage change
perc_change_odds = (np.exp(logit_2.params) - 1) * 100

# removing limit from number of columns to display
pd.set_option("display.max_columns", None)

# adding the odds to a dataframe
pd.DataFrame({"Odds": odds, "Change_odd%": perc_change_odds}, index=x_train2.columns).T

	const	no_of_adults	no_of_children	no_of_weekend_nights	no_of_week_nights	type_of_meal_plan	required_car_parking_space	lead_time	arrival_year	arrival_month	repeated_guest	no_of_previous_cancellations	avg_price_per_room	no_of_special_requests	room_type_reserved_Room_Type 2	room_type_reserved_Room_Type 4	room_type_reserved_Room_Type 5	room_type_reserved_Room_Type 6	room_type_reserved_Room_Type 7	market_segment_type_Corporate	market_segment_type_Offline
Odds	23.768449	0.903103	0.856671	0.885807	0.956743	1.110674	4.936749	0.984452	0.645402	1.044772	12.406560	0.816965	0.979317	4.388422	1.427715	1.354321	2.103071	2.202893	2.419719	2.138650	5.682613
Change_odd%	2276.844935	-9.689663	-14.332892	-11.419324	-4.325735	11.067378	393.674854	-1.554765	-35.459758	4.477246	1140.655953	-18.303473	-2.068268	338.842242	42.771516	35.432070	110.307117	120.289290	141.971879	113.865016	468.261279

Coefficient interpretations

• market_segment_type_Offline: Holding all other features constant a 1 unit change in market_segment_type_Offline will increase the odds of a customer not-cancel by ~5.7 times or a ~468 increase in odds of not-cancelling.
• required_car_parking_space: Holding all other features constant a 1 unit change in market_segment_type_Offline will increase the odds of a customer not-cancel by ~4.9 times or a ~393% increase in odds of not-cancelling.

Model performance evaluation

Training set performance

confusion_matrix_statsmodels(logit_2, x_train2, y_train)

log_reg_model_train_perf=model_performance_classification_statsmodels(logit_2, x_train2, y_train)
print("Training modelperformance")
log_reg_model_train_perf

Training modelperformance

	Accuracy	Recall	Precision	F1
0	0.805888	0.890092	0.832566	0.860369

Test set performance

confusion_matrix_statsmodels(logit_2, x_test2, y_test)

log_reg_model_test_perf=model_performance_classification_statsmodels(logit_2, x_test2, y_test)
print("Training modelperformance")
log_reg_model_test_perf

Training modelperformance

	Accuracy	Recall	Precision	F1
0	0.803948	0.889707	0.831218	0.859469

• The model is giving a good f1_score of ~0.860 and ~0.859 on the train and test sets respectively
• As the train and test performances are comparable, the model is not overfitting
• Moving forward we will try to improve the performance of the model

Model Performance Improvement

Let's see if the f1_score can be improved further by changing the model threshold First, we will check the ROC curve, compute the area under the ROC curve (ROC-AUC), and then use it to find the optimal threshold Next, we will check the Precision-Recall curve to find the right balance between precision and recall as our metric of choice is f1_score

ROC Curve and ROC-AUC

ROC-AUC on training set

roc_score_train=roc_auc_score(y_train,logit_2.predict(x_train2))
fpr, tpr, thresholds=roc_curve(y_train,logit_2.predict(x_train2))
plt.figure(figsize=(7, 5))
plt.plot(fpr, tpr, label="Logistic Regression (area = %0.2f)" % roc_score_train)
plt.plot([0, 1], [0, 1], "r--")
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")
plt.title("Receiver operating characteristic")
plt.legend(loc="lower right")
plt.show()

optimal_idx = np.argmax(tpr - fpr)
optimal_threshold_auc_roc = thresholds[optimal_idx]
print(optimal_threshold_auc_roc)

0.6309643264462703

confusion_matrix_statsmodels(
    logit_2, x_train2, y_train, threshold=optimal_threshold_auc_roc
)

# checking model performance for this model
log_reg_model_train_perf_threshold_auc_roc = model_performance_classification_statsmodels(
    logit_2, x_train2, y_train, threshold=optimal_threshold_auc_roc
)
print("Test performance:")
log_reg_model_train_perf_threshold_auc_roc

Test performance:

	Accuracy	Recall	Precision	F1
0	0.792141	0.820778	0.863134	0.841423

roc- curve Test-sample

roc_score_test=roc_auc_score(y_test,logit_2.predict(x_test2))
fpr,tpr,threshold=roc_curve(y_test,logit_2.predict(x_test2))
plt.figure(figsize=(7, 5))
plt.plot(fpr, tpr, label="Logistic Regression (area = %0.2f)" % roc_score_test)
plt.plot([0, 1], [0, 1], "r--")
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")
plt.title("Receiver operating characteristic")
plt.legend(loc="lower right")
plt.show()

confusion_matrix_statsmodels(
    logit_2, x_test2, y_test, threshold=optimal_threshold_auc_roc
)

log_reg_model_test_perf_threshold_auc_roc=model_performance_classification_statsmodels(
    logit_2, x_test2, y_test, threshold=optimal_threshold_auc_roc
)
print("testing modelperformance")
log_reg_model_test_perf_threshold_auc_roc

testing modelperformance

	Accuracy	Recall	Precision	F1
0	0.794575	0.824415	0.864447	0.843957

y_scores = logit_2.predict(x_train2)
prec, rec, tre = precision_recall_curve(y_train, y_scores,)


def plot_prec_recall_vs_tresh(precisions, recalls, thresholds):
    plt.plot(thresholds, precisions[:-1], "b--", label="precision")
    plt.plot(thresholds, recalls[:-1], "g--", label="recall")
    plt.xlabel("Threshold")
    plt.legend(loc="upper left")
    plt.ylim([0, 1])
    


plt.figure(figsize=(10, 7))
plot_prec_recall_vs_tresh(prec, rec, tre)
plt.show()

optimal_threshold_curve = 0.59

• The Threshold 0.59 we will get the balance threshold value

confusion_matrix_statsmodels(
    logit_2, x_train2, y_train, threshold=optimal_threshold_curve
)

log_reg_model_train_perf_threshold_curve = model_performance_classification_statsmodels(
    logit_2, x_train2, y_train, threshold=optimal_threshold_curve
)
print("Training performance:")
log_reg_model_train_perf_threshold_curve

Training performance:

	Accuracy	Recall	Precision	F1
0	0.799015	0.845615	0.853836	0.849706

Model is performing well on training set. There's not much improvement in the model performance as the default threshold is 0.63 and here we get 0.59 as the optimal threshold.

confusion_matrix_statsmodels(
    logit_2, x_test2, y_test, threshold=optimal_threshold_curve
)

log_reg_model_test_perf_threshold_curve = model_performance_classification_statsmodels(
    logit_2, x_test2, y_test, threshold=optimal_threshold_curve
)
print("Testing performance:")
log_reg_model_test_perf_threshold_curve

Testing performance:

	Accuracy	Recall	Precision	F1
0	0.802845	0.849779	0.856507	0.85313

Model Performance Comparison and Final Model Selection

# training performance comparison

models_train_comp_df = pd.concat(
    [
        log_reg_model_train_perf.T,
        log_reg_model_train_perf_threshold_auc_roc.T,
        log_reg_model_train_perf_threshold_curve.T,
    ],
    axis=1,
)
models_train_comp_df.columns = [
    "Logistic Regression-default Threshold (0.5)",
    "Logistic Regression-0.63 Threshold",
    "Logistic Regression-0.59 Threshold",
]

print("Training performance comparison:")
models_train_comp_df

Training performance comparison:

	Logistic Regression-default Threshold (0.5)	Logistic Regression-0.63 Threshold	Logistic Regression-0.59 Threshold
Accuracy	0.805888	0.792141	0.799015
Recall	0.890092	0.820778	0.845615
Precision	0.832566	0.863134	0.853836
F1	0.860369	0.841423	0.849706

# test performance comparison

models_train_comp_df = pd.concat(
    [
        log_reg_model_test_perf.T,
        log_reg_model_test_perf_threshold_auc_roc.T,
        log_reg_model_test_perf_threshold_curve.T,
    ],
    axis=1,
)
models_train_comp_df.columns = [
    "Logistic Regression-default Threshold (0.5)",
    "Logistic Regression-0.63 Threshold",
    "Logistic Regression-0.59 Threshold",
]

print("Training performance comparison:")
models_train_comp_df

Training performance comparison:

	Logistic Regression-default Threshold (0.5)	Logistic Regression-0.63 Threshold	Logistic Regression-0.59 Threshold
Accuracy	0.803948	0.794575	0.802845
Recall	0.889707	0.824415	0.849779
Precision	0.831218	0.864447	0.856507
F1	0.859469	0.843957	0.853130

• Almost all the three models are performing well on both training and test data without the problem of overfitting
• The model with a default threshold (0.5) is giving the best F1 score. Therefore it can be selected as the final model

Conclusions and Recommendations

• We have been able to build a predictive model that can be used INN Hotels to find the customers cancelled with an f1_score of 0.85 on the training set and formulate policies accordingly.

• All the logistic regression models have given a generalized performance on the training and test set.

• Coefficient of some levels of required_car_parking_space, repeated_guest,no_of_special_requests and market_segment_type_Offline are positive an increase in these will lead to increase in chances of a customer not-Cancel.

• required_car_parking_space: Holding all other features constant a 1 unit change in market_segment_type_Offline will increase the odds of a customer not-cancel by ~4.9 times or a ~393% increase in odds of not-cancelling.

• INN Hotels need to reduse the lead time that would reduce the chance of the cancelation. People book earlies has highest chance of cancelation.

• As the Number of the gust and the childern increase there is high possible chance of cancelation.

Building a Decision Tree model

For the decision Tree model the outliers will not effect the results so we have to build the X and Y and the train and the test

def model_performance_classification_sklearn(model, predictors, target):
    """
    Function to compute different metrics to check classification model performance

    model: classifier
    predictors: independent variables
    target: dependent variable
    """

    # predicting using the independent variables
    pred = model.predict(predictors)

    acc = accuracy_score(target, pred)  # to compute Accuracy
    recall = recall_score(target, pred)  # to compute Recall
    precision = precision_score(target, pred)  # to compute Precision
    f1 = f1_score(target, pred)  # to compute F1-score

    # creating a dataframe of metrics
    df_perf = pd.DataFrame(
        {"Accuracy": acc, "Recall": recall, "Precision": precision, "F1": f1,},
        index=[0],
    )

    return df_perf

def confusion_matrix_sklearn(model, predictors, target):
    """
    To plot the confusion_matrix with percentages

    model: classifier
    predictors: independent variables
    target: dependent variable
    """
    y_pred = model.predict(predictors)
    cm = confusion_matrix(target, y_pred)
    labels = np.asarray(
        [
            ["{0:0.0f}".format(item) + "\n{0:.2%}".format(item / cm.flatten().sum())]
            for item in cm.flatten()
        ]
    ).reshape(2, 2)

    plt.figure(figsize=(6, 4))
    sns.heatmap(cm, annot=labels, fmt="")
    plt.ylabel("True label")
    plt.xlabel("Predicted label")

data=data.replace({"booking_status":{"Not_Canceled":1,"Canceled":0}})
X=data.drop("booking_status",axis=1)
Y=data["booking_status"]
X=pd.get_dummies(X,columns=["type_of_meal_plan","room_type_reserved","market_segment_type"],dtype=float)
X

	no_of_adults	no_of_children	no_of_weekend_nights	no_of_week_nights	required_car_parking_space	lead_time	arrival_year	arrival_month	arrival_date	repeated_guest	no_of_previous_cancellations	no_of_previous_bookings_not_canceled	avg_price_per_room	no_of_special_requests	type_of_meal_plan_Meal Plan 1	type_of_meal_plan_Meal Plan 2	type_of_meal_plan_Meal Plan 3	type_of_meal_plan_Not Selected	room_type_reserved_Room_Type 1	room_type_reserved_Room_Type 2	room_type_reserved_Room_Type 3	room_type_reserved_Room_Type 4	room_type_reserved_Room_Type 5	room_type_reserved_Room_Type 6	room_type_reserved_Room_Type 7	market_segment_type_Aviation	market_segment_type_Complementary	market_segment_type_Corporate	market_segment_type_Offline	market_segment_type_Online
0	2	0	1	2	0	224	2017	10	2	0	0	0	65.00	0	1.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0
1	2	0	2	3	0	5	2018	11	6	0	0	0	106.68	1	0.0	0.0	0.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0
2	1	0	2	1	0	1	2018	2	28	0	0	0	60.00	0	1.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0
3	2	0	0	2	0	211	2018	5	20	0	0	0	100.00	0	1.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0
4	2	0	1	1	0	48	2018	4	11	0	0	0	94.50	0	0.0	0.0	0.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
36270	3	0	2	6	0	85	2018	8	3	0	0	0	167.80	1	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0
36271	2	0	1	3	0	228	2018	10	17	0	0	0	90.95	2	1.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0
36272	2	0	2	6	0	148	2018	7	1	0	0	0	98.39	2	1.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0
36273	2	0	0	3	0	63	2018	4	21	0	0	0	94.50	0	0.0	0.0	0.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0
36274	2	0	1	2	0	207	2018	12	30	0	0	0	161.67	0	1.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0

36275 rows × 30 columns

X_train_1,X_test_1,y_train_1,y_test_1=train_test_split(X,Y,random_state=1,test_size=.25)

model0=DecisionTreeClassifier(random_state=1)
model0.fit(X_train_1,y_train_1)

DecisionTreeClassifier(random_state=1)

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Model Evaluation

Model evaluation criterion

Model can make wrong predictions as:

• Predicting a customer will not cancel but in reality, the customer will cancel (FN)
• Predicting a customer will cancel but in reality, the customer will not cancel (FP)

Which case is more important?

• If we predict that a customer will not cancel but in reality, the customer cancel, then the company will have to bear the cost loss of booking.

• If we predict that a customer will cancel but in reality, the customer does not cancel, then the company will have to accomidated the room for the customer.

• Booking by the customer and canceled that would cost more for the company

How to reduce the losses?

The company would want the recall to be maximized, greater the recall score higher are the chances of minimizing the False Negatives.

DecisionTree for train data

decision_tree_perf_train_without=model_performance_classification_sklearn(model0,X_train_1,y_train_1)
decision_tree_perf_train_without

	Accuracy	Recall	Precision	F1
0	0.993972	0.995569	0.99546	0.995514

confusion_matrix_sklearn(model0,X_train_1,y_train)

DecisionTree for Test data

decision_tree_perf_test_without=model_performance_classification_sklearn(model0,X_test_1,y_test_1)
decision_tree_perf_test_without

	Accuracy	Recall	Precision	F1
0	0.870438	0.898544	0.908204	0.903348

confusion_matrix_sklearn(model0,X_test_1,y_test_1)

• Test and the train data results having with high difference with ~0.10

feature_name=X_train_1.columns.to_list()

plt.figure(figsize=(20,30))
tree.plot_tree(model0, 
    feature_names=feature_name,
    class_names=None,
    filled=True,
    impurity=True,
    node_ids=True,
    proportion=False,
    rounded=False,
    precision=3,
    fontsize=8);

print(tree.export_text(model0,feature_names=feature_name,show_weights=True))

|--- lead_time <= 151.50
|   |--- no_of_special_requests <= 0.50
|   |   |--- market_segment_type_Online <= 0.50
|   |   |   |--- lead_time <= 90.50
|   |   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |   |--- avg_price_per_room <= 179.47
|   |   |   |   |   |   |--- market_segment_type_Offline <= 0.50
|   |   |   |   |   |   |   |--- lead_time <= 16.50
|   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 162.09
|   |   |   |   |   |   |   |   |   |   |--- repeated_guest <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 15
|   |   |   |   |   |   |   |   |   |   |--- repeated_guest >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 153.00] class: 1
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  162.09
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 10.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  10.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 29.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10
|   |   |   |   |   |   |   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |--- arrival_date >  29.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 5.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  5.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- lead_time >  16.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 135.00
|   |   |   |   |   |   |   |   |   |--- lead_time <= 86.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 12
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 32.00] class: 1
|   |   |   |   |   |   |   |   |   |--- lead_time >  86.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_year <= 2017.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [4.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_year >  2017.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  135.00
|   |   |   |   |   |   |   |   |   |--- weights: [8.00, 0.00] class: 0
|   |   |   |   |   |   |--- market_segment_type_Offline >  0.50
|   |   |   |   |   |   |   |--- weights: [0.00, 1728.00] class: 1
|   |   |   |   |   |--- avg_price_per_room >  179.47
|   |   |   |   |   |   |--- lead_time <= 22.00
|   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1
|   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |--- lead_time >  22.00
|   |   |   |   |   |   |   |--- arrival_date <= 25.50
|   |   |   |   |   |   |   |   |--- weights: [18.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- arrival_date >  25.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |   |--- lead_time <= 68.50
|   |   |   |   |   |   |--- no_of_weekend_nights <= 4.50
|   |   |   |   |   |   |   |--- arrival_date <= 27.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 9.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 59.50
|   |   |   |   |   |   |   |   |   |   |--- market_segment_type_Aviation <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 17
|   |   |   |   |   |   |   |   |   |   |--- market_segment_type_Aviation >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |   |--- lead_time >  59.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 19.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 23.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  19.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |--- arrival_month >  9.50
|   |   |   |   |   |   |   |   |   |--- market_segment_type_Aviation <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 65.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 11
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  65.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |--- market_segment_type_Aviation >  0.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 7.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  7.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |--- arrival_date >  27.50
|   |   |   |   |   |   |   |   |--- lead_time <= 1.50
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 77.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 9.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  77.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 4.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [36.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  4.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |   |--- lead_time >  1.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 41.08
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  41.08
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 116.30
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  116.30
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |--- no_of_weekend_nights >  4.50
|   |   |   |   |   |   |   |--- weights: [10.00, 0.00] class: 0
|   |   |   |   |   |--- lead_time >  68.50
|   |   |   |   |   |   |--- avg_price_per_room <= 99.98
|   |   |   |   |   |   |   |--- arrival_month <= 3.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 62.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 21.00] class: 1
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  62.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 77.00
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 23.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [10.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  23.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |   |--- lead_time >  77.00
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 9.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |--- arrival_month >  3.50
|   |   |   |   |   |   |   |   |--- lead_time <= 71.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 8.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  8.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1
|   |   |   |   |   |   |   |   |--- lead_time >  71.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 87.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 45.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |--- lead_time >  87.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 77.88
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 9.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  77.88
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |--- avg_price_per_room >  99.98
|   |   |   |   |   |   |   |--- lead_time <= 81.00
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 123.25
|   |   |   |   |   |   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [53.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 21.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  21.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  123.25
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 10.00] class: 1
|   |   |   |   |   |   |   |--- lead_time >  81.00
|   |   |   |   |   |   |   |   |--- lead_time <= 88.50
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 4.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 17.00] class: 1
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  4.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 110.62
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  110.62
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |--- lead_time >  88.50
|   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |--- lead_time >  90.50
|   |   |   |   |--- lead_time <= 116.50
|   |   |   |   |   |--- avg_price_per_room <= 93.58
|   |   |   |   |   |   |--- arrival_date <= 6.50
|   |   |   |   |   |   |   |--- no_of_week_nights <= 2.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 3.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 4.00] class: 1
|   |   |   |   |   |   |   |   |--- arrival_month >  3.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 80.38
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 1 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 1 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  80.38
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 1 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 1 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |--- no_of_week_nights >  2.50
|   |   |   |   |   |   |   |   |--- arrival_date <= 5.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 39.00] class: 1
|   |   |   |   |   |   |   |   |--- arrival_date >  5.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 75.22
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  75.22
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 1 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 1 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |--- arrival_date >  6.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 66.50
|   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 1 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 97.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  97.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 1 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 26.00] class: 1
|   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 58.75
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 5.00] class: 1
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  58.75
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 97.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  97.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [41.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- avg_price_per_room >  66.50
|   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 1 <= 0.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 82.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [8.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  82.50
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 1 >  0.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 28.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 3.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  3.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_date >  28.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 96.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 12.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  96.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |--- avg_price_per_room >  93.58
|   |   |   |   |   |   |--- arrival_date <= 16.50
|   |   |   |   |   |   |   |--- arrival_month <= 7.50
|   |   |   |   |   |   |   |   |--- lead_time <= 108.50
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  0.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 125.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  125.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- lead_time >  108.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 111.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 5.00] class: 1
|   |   |   |   |   |   |   |   |   |--- lead_time >  111.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 12.00] class: 1
|   |   |   |   |   |   |   |--- arrival_month >  7.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 108.50
|   |   |   |   |   |   |   |   |   |--- arrival_year <= 2017.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [48.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_year >  2017.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 113.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  113.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  108.50
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 45.00] class: 1
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 9.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  9.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |--- arrival_date >  16.50
|   |   |   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 127.39
|   |   |   |   |   |   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [53.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  127.39
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 101.34
|   |   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  101.34
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 29.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  29.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 8.00] class: 1
|   |   |   |   |--- lead_time >  116.50
|   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |--- arrival_month <= 3.50
|   |   |   |   |   |   |   |--- weights: [0.00, 42.00] class: 1
|   |   |   |   |   |   |--- arrival_month >  3.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 93.58
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 65.38
|   |   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  65.38
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 89.88
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 25.00] class: 1
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  89.88
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 90.47
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 8.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  90.47
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |--- avg_price_per_room >  93.58
|   |   |   |   |   |   |   |   |--- lead_time <= 145.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 7.00
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 8.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 15.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  8.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- arrival_month >  7.00
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 141.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  141.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 14.00] class: 1
|   |   |   |   |   |   |   |   |--- lead_time >  145.50
|   |   |   |   |   |   |   |   |   |--- no_of_adults <= 2.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [16.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- no_of_adults >  2.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 1.00] class: 0
|   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |   |   |--- arrival_date <= 26.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 113.00] class: 1
|   |   |   |   |   |   |   |--- arrival_date >  26.50
|   |   |   |   |   |   |   |   |--- arrival_date <= 28.50
|   |   |   |   |   |   |   |   |   |--- weights: [1.00, 2.00] class: 1
|   |   |   |   |   |   |   |   |--- arrival_date >  28.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 5.00] class: 1
|   |   |   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |   |   |--- lead_time <= 125.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 90.47
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 88.17
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 3.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  3.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  88.17
|   |   |   |   |   |   |   |   |   |   |--- weights: [10.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  90.47
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 16.00] class: 1
|   |   |   |   |   |   |   |--- lead_time >  125.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 155.78
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 19.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 10.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  10.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |   |   |--- arrival_date >  19.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 128.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  128.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 81.00] class: 1
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  155.78
|   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |--- market_segment_type_Online >  0.50
|   |   |   |--- lead_time <= 13.50
|   |   |   |   |--- avg_price_per_room <= 119.42
|   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |--- arrival_month <= 1.50
|   |   |   |   |   |   |   |--- weights: [0.00, 137.00] class: 1
|   |   |   |   |   |   |--- arrival_month >  1.50
|   |   |   |   |   |   |   |--- lead_time <= 3.50
|   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 2.50
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- arrival_month >  2.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 74.57
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 33.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  74.57
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 13
|   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 75.46
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [13.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  75.46
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |--- lead_time >  3.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 99.38
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 3.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 16.00] class: 1
|   |   |   |   |   |   |   |   |   |--- arrival_date >  3.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 12.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  12.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 10.00] class: 1
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  99.38
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 117.25
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 4.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [6.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  4.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  117.25
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 3.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  3.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 12.00] class: 1
|   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |--- no_of_week_nights <= 4.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 117.56
|   |   |   |   |   |   |   |   |--- arrival_year <= 2017.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 157.00] class: 1
|   |   |   |   |   |   |   |   |--- arrival_year >  2017.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 8.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  8.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 76.00] class: 1
|   |   |   |   |   |   |   |--- avg_price_per_room >  117.56
|   |   |   |   |   |   |   |   |--- arrival_month <= 9.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 119.10
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 3.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  3.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [4.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  119.10
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |   |--- arrival_month >  9.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 4.00] class: 1
|   |   |   |   |   |   |--- no_of_week_nights >  4.50
|   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |--- weights: [5.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 6.00] class: 1
|   |   |   |   |--- avg_price_per_room >  119.42
|   |   |   |   |   |--- lead_time <= 3.50
|   |   |   |   |   |   |--- avg_price_per_room <= 178.78
|   |   |   |   |   |   |   |--- no_of_week_nights <= 4.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 5.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 2.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 22.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 20.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  22.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |--- lead_time >  2.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 132.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  132.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [4.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_month >  5.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 9.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |--- arrival_month >  9.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 52.00] class: 1
|   |   |   |   |   |   |   |--- no_of_week_nights >  4.50
|   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0
|   |   |   |   |   |   |--- avg_price_per_room >  178.78
|   |   |   |   |   |   |   |--- arrival_date <= 15.50
|   |   |   |   |   |   |   |   |--- arrival_date <= 10.50
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 6.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  6.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 6.00] class: 1
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_date >  10.50
|   |   |   |   |   |   |   |   |   |--- weights: [8.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- arrival_date >  15.50
|   |   |   |   |   |   |   |   |--- no_of_week_nights <= 3.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 24.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 22.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  22.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_date >  24.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 7.00] class: 1
|   |   |   |   |   |   |   |   |--- no_of_week_nights >  3.50
|   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |--- lead_time >  3.50
|   |   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |   |--- arrival_year <= 2017.50
|   |   |   |   |   |   |   |   |--- no_of_week_nights <= 0.50
|   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- no_of_week_nights >  0.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 4.00] class: 1
|   |   |   |   |   |   |   |--- arrival_year >  2017.50
|   |   |   |   |   |   |   |   |--- no_of_adults <= 2.50
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 5 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 160.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  160.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 5 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |   |--- no_of_adults >  2.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 5.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 4.00] class: 1
|   |   |   |   |   |   |   |   |   |--- lead_time >  5.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [6.00, 0.00] class: 0
|   |   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |   |--- arrival_date <= 14.00
|   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |--- arrival_year <= 2017.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 2.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  2.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 5.00] class: 1
|   |   |   |   |   |   |   |   |   |--- arrival_year >  2017.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 7.00] class: 1
|   |   |   |   |   |   |   |--- arrival_date >  14.00
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 161.58
|   |   |   |   |   |   |   |   |   |--- lead_time <= 12.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 24.00] class: 1
|   |   |   |   |   |   |   |   |   |--- lead_time >  12.50
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 1 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 1 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  161.58
|   |   |   |   |   |   |   |   |   |--- arrival_year <= 2017.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1
|   |   |   |   |   |   |   |   |   |--- arrival_year >  2017.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 166.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  166.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |--- lead_time >  13.50
|   |   |   |   |--- avg_price_per_room <= 105.27
|   |   |   |   |   |--- avg_price_per_room <= 60.19
|   |   |   |   |   |   |--- lead_time <= 84.50
|   |   |   |   |   |   |   |--- arrival_date <= 17.50
|   |   |   |   |   |   |   |   |--- lead_time <= 54.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 44.00
|   |   |   |   |   |   |   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 10.00] class: 1
|   |   |   |   |   |   |   |   |   |--- lead_time >  44.00
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |--- lead_time >  54.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 19.00] class: 1
|   |   |   |   |   |   |   |--- arrival_date >  17.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 32.00] class: 1
|   |   |   |   |   |   |--- lead_time >  84.50
|   |   |   |   |   |   |   |--- arrival_year <= 2017.50
|   |   |   |   |   |   |   |   |--- arrival_date <= 19.00
|   |   |   |   |   |   |   |   |   |--- lead_time <= 139.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [8.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- lead_time >  139.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |--- arrival_date >  19.00
|   |   |   |   |   |   |   |   |   |--- lead_time <= 87.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- lead_time >  87.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 6.00] class: 1
|   |   |   |   |   |   |   |--- arrival_year >  2017.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 59.43
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 16.00] class: 1
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  59.43
|   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |--- avg_price_per_room >  60.19
|   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 1 <= 0.50
|   |   |   |   |   |   |   |--- lead_time <= 25.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 8.00] class: 1
|   |   |   |   |   |   |   |   |   |--- arrival_month >  1.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 25.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  25.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 4.00] class: 1
|   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 18.00] class: 1
|   |   |   |   |   |   |   |--- lead_time >  25.50
|   |   |   |   |   |   |   |   |--- required_car_parking_space <= 0.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 87.97
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 4.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  4.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 14
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  87.97
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 2 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 17
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 2 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |   |--- required_car_parking_space >  0.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 6.00] class: 1
|   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 1 >  0.50
|   |   |   |   |   |   |   |--- arrival_year <= 2017.50
|   |   |   |   |   |   |   |   |--- lead_time <= 60.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 7.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 8.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  8.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  7.50
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 2 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 2 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |--- lead_time >  60.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 80.72
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 9.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  9.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  80.72
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |--- arrival_year >  2017.50
|   |   |   |   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 28.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 6.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  28.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- arrival_month >  1.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 15
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 33.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 17.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |   |   |--- lead_time >  33.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 71.80
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  71.80
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 27
|   |   |   |   |--- avg_price_per_room >  105.27
|   |   |   |   |   |--- required_car_parking_space <= 0.50
|   |   |   |   |   |   |--- arrival_year <= 2017.50
|   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 <= 0.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 9.50
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 3.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 20.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  20.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 5.00] class: 1
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  3.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_month >  9.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 30.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 27.00] class: 1
|   |   |   |   |   |   |   |   |   |--- arrival_date >  30.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 25.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  25.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 >  0.50
|   |   |   |   |   |   |   |   |--- weights: [13.00, 0.00] class: 0
|   |   |   |   |   |   |--- arrival_year >  2017.50
|   |   |   |   |   |   |   |--- arrival_month <= 10.50
|   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 5 <= 0.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 144.76
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 24.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 21
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  24.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 18
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  144.76
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 91.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 18
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  91.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10
|   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 5 >  0.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 26.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 175.71
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  175.71
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_date >  26.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [5.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- arrival_month >  10.50
|   |   |   |   |   |   |   |   |--- lead_time <= 46.00
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 168.06
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 147.95
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  147.95
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  168.06
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 2.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |--- lead_time >  46.00
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 3.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 175.65
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  175.65
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  3.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1
|   |   |   |   |   |--- required_car_parking_space >  0.50
|   |   |   |   |   |   |--- no_of_weekend_nights <= 3.00
|   |   |   |   |   |   |   |--- weights: [0.00, 46.00] class: 1
|   |   |   |   |   |   |--- no_of_weekend_nights >  3.00
|   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |--- no_of_special_requests >  0.50
|   |   |--- no_of_special_requests <= 1.50
|   |   |   |--- market_segment_type_Online <= 0.50
|   |   |   |   |--- lead_time <= 102.50
|   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |--- no_of_weekend_nights <= 4.00
|   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 5 <= 0.50
|   |   |   |   |   |   |   |   |--- lead_time <= 91.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 129.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 903.00] class: 1
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  129.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 131.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  131.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 29.00] class: 1
|   |   |   |   |   |   |   |   |--- lead_time >  91.50
|   |   |   |   |   |   |   |   |   |--- no_of_children <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 44.00] class: 1
|   |   |   |   |   |   |   |   |   |--- no_of_children >  0.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 16.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  16.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 5 >  0.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 164.79
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 138.55
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 11.00] class: 1
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  138.55
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 23.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  23.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  164.79
|   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |--- no_of_weekend_nights >  4.00
|   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |--- lead_time <= 63.00
|   |   |   |   |   |   |   |--- market_segment_type_Corporate <= 0.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 18.00] class: 1
|   |   |   |   |   |   |   |--- market_segment_type_Corporate >  0.50
|   |   |   |   |   |   |   |   |--- lead_time <= 12.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |--- lead_time >  12.50
|   |   |   |   |   |   |   |   |   |--- weights: [1.00, 2.00] class: 1
|   |   |   |   |   |   |--- lead_time >  63.00
|   |   |   |   |   |   |   |--- weights: [5.00, 0.00] class: 0
|   |   |   |   |--- lead_time >  102.50
|   |   |   |   |   |--- no_of_week_nights <= 2.50
|   |   |   |   |   |   |--- lead_time <= 105.00
|   |   |   |   |   |   |   |--- avg_price_per_room <= 67.65
|   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |--- avg_price_per_room >  67.65
|   |   |   |   |   |   |   |   |--- weights: [4.00, 0.00] class: 0
|   |   |   |   |   |   |--- lead_time >  105.00
|   |   |   |   |   |   |   |--- avg_price_per_room <= 83.39
|   |   |   |   |   |   |   |   |--- arrival_month <= 3.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 6.00] class: 1
|   |   |   |   |   |   |   |   |--- arrival_month >  3.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 6.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1
|   |   |   |   |   |   |   |   |   |--- arrival_date >  6.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 4.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  4.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [5.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- avg_price_per_room >  83.39
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 141.25
|   |   |   |   |   |   |   |   |   |--- lead_time <= 143.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 25.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 24.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  25.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- lead_time >  143.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 5.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  5.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  141.25
|   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |--- no_of_week_nights >  2.50
|   |   |   |   |   |   |--- avg_price_per_room <= 169.69
|   |   |   |   |   |   |   |--- avg_price_per_room <= 122.00
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 97.33
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 48.00] class: 1
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  97.33
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 1 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 1.00] class: 0
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 1 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 12.00] class: 1
|   |   |   |   |   |   |   |--- avg_price_per_room >  122.00
|   |   |   |   |   |   |   |   |--- arrival_month <= 7.50
|   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_month >  7.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1
|   |   |   |   |   |   |--- avg_price_per_room >  169.69
|   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |--- market_segment_type_Online >  0.50
|   |   |   |   |--- lead_time <= 8.50
|   |   |   |   |   |--- lead_time <= 4.50
|   |   |   |   |   |   |--- no_of_week_nights <= 10.00
|   |   |   |   |   |   |   |--- avg_price_per_room <= 157.93
|   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 2 <= 0.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 27.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 26.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 14
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  26.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |--- arrival_date >  27.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 75.00] class: 1
|   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 2 >  0.50
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 2.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 5.00] class: 1
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  2.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 2.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  2.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |--- avg_price_per_room >  157.93
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 158.50
|   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  158.50
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 179.28
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  179.28
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 173.78
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  173.78
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1
|   |   |   |   |   |   |--- no_of_week_nights >  10.00
|   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |--- lead_time >  4.50
|   |   |   |   |   |   |--- room_type_reserved_Room_Type 2 <= 0.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 118.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 13.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 4.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  4.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |--- arrival_date >  13.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 5.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  5.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 90.00] class: 1
|   |   |   |   |   |   |   |--- avg_price_per_room >  118.50
|   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 15.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 7.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 11
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  7.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- arrival_date >  15.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 120.30
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  120.30
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- repeated_guest <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- repeated_guest >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 28.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  28.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |--- room_type_reserved_Room_Type 2 >  0.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 94.48
|   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- avg_price_per_room >  94.48
|   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |--- lead_time >  8.50
|   |   |   |   |   |--- required_car_parking_space <= 0.50
|   |   |   |   |   |   |--- avg_price_per_room <= 127.62
|   |   |   |   |   |   |   |--- no_of_weekend_nights <= 2.50
|   |   |   |   |   |   |   |   |--- lead_time <= 43.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 95.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 22
|   |   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 139.00] class: 1
|   |   |   |   |   |   |   |   |--- lead_time >  43.50
|   |   |   |   |   |   |   |   |   |--- arrival_year <= 2017.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 7.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  7.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 11
|   |   |   |   |   |   |   |   |   |--- arrival_year >  2017.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 19
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 20
|   |   |   |   |   |   |   |--- no_of_weekend_nights >  2.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 119.12
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1
|   |   |   |   |   |   |   |   |   |--- arrival_month >  1.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 8.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  8.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [13.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  119.12
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1
|   |   |   |   |   |   |--- avg_price_per_room >  127.62
|   |   |   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |   |   |--- arrival_date <= 19.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 177.15
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 11.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  11.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 16
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  177.15
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 7.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 10.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  7.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |--- arrival_date >  19.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 27.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 23.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  23.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 13
|   |   |   |   |   |   |   |   |   |--- arrival_date >  27.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 88.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  88.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |   |   |--- lead_time <= 141.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_year <= 2017.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |   |   |--- arrival_year >  2017.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 19
|   |   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 100.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 53.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  100.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |--- lead_time >  141.50
|   |   |   |   |   |   |   |   |   |--- weights: [10.00, 0.00] class: 0
|   |   |   |   |   |--- required_car_parking_space >  0.50
|   |   |   |   |   |   |--- no_of_weekend_nights <= 3.00
|   |   |   |   |   |   |   |--- lead_time <= 150.00
|   |   |   |   |   |   |   |   |--- weights: [0.00, 194.00] class: 1
|   |   |   |   |   |   |   |--- lead_time >  150.00
|   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |--- no_of_weekend_nights >  3.00
|   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |--- no_of_special_requests >  1.50
|   |   |   |--- lead_time <= 90.50
|   |   |   |   |--- no_of_week_nights <= 3.50
|   |   |   |   |   |--- weights: [0.00, 2294.00] class: 1
|   |   |   |   |--- no_of_week_nights >  3.50
|   |   |   |   |   |--- no_of_week_nights <= 9.50
|   |   |   |   |   |   |--- no_of_special_requests <= 2.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 80.80
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 71.75
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 10.00] class: 1
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  71.75
|   |   |   |   |   |   |   |   |   |--- lead_time <= 13.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 6.00] class: 1
|   |   |   |   |   |   |   |   |   |--- lead_time >  13.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 6.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 4.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  6.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |--- avg_price_per_room >  80.80
|   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 6.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_children <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 29.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- no_of_children >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- lead_time >  6.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 19.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  19.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 12
|   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 32.00] class: 1
|   |   |   |   |   |   |--- no_of_special_requests >  2.50
|   |   |   |   |   |   |   |--- weights: [0.00, 74.00] class: 1
|   |   |   |   |   |--- no_of_week_nights >  9.50
|   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0
|   |   |   |--- lead_time >  90.50
|   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |--- lead_time <= 150.50
|   |   |   |   |   |   |--- arrival_year <= 2017.50
|   |   |   |   |   |   |   |--- arrival_month <= 7.50
|   |   |   |   |   |   |   |   |--- arrival_date <= 4.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |--- arrival_date >  4.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 26.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [5.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_date >  26.00
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 28.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  28.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- arrival_month >  7.50
|   |   |   |   |   |   |   |   |--- arrival_date <= 14.00
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 11.00] class: 1
|   |   |   |   |   |   |   |   |--- arrival_date >  14.00
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 4.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  4.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |--- arrival_year >  2017.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 157.65
|   |   |   |   |   |   |   |   |--- no_of_children <= 0.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 29.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 25.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  25.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |--- arrival_date >  29.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 2.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |--- no_of_children >  0.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 4.50
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  4.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 4.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  4.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |--- avg_price_per_room >  157.65
|   |   |   |   |   |   |   |   |--- no_of_children <= 2.50
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 4.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 10.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  10.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  4.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 97.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  97.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- no_of_children >  2.50
|   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |--- lead_time >  150.50
|   |   |   |   |   |   |--- avg_price_per_room <= 103.50
|   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |--- avg_price_per_room >  103.50
|   |   |   |   |   |   |   |--- weights: [5.00, 0.00] class: 0
|   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |--- no_of_special_requests <= 2.50
|   |   |   |   |   |   |--- avg_price_per_room <= 153.15
|   |   |   |   |   |   |   |--- avg_price_per_room <= 73.53
|   |   |   |   |   |   |   |   |--- no_of_week_nights <= 3.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 4.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_date >  4.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 121.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 15.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  121.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |--- no_of_week_nights >  3.50
|   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- avg_price_per_room >  73.53
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 90.42
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 123.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  123.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 21.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  21.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  90.42
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 148.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  148.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |--- avg_price_per_room >  153.15
|   |   |   |   |   |   |   |--- arrival_date <= 26.50
|   |   |   |   |   |   |   |   |--- lead_time <= 148.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 17.00] class: 1
|   |   |   |   |   |   |   |   |--- lead_time >  148.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 163.03
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  163.03
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |--- arrival_date >  26.50
|   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |--- no_of_special_requests >  2.50
|   |   |   |   |   |   |--- weights: [0.00, 55.00] class: 1
|--- lead_time >  151.50
|   |--- avg_price_per_room <= 100.04
|   |   |--- no_of_special_requests <= 0.50
|   |   |   |--- market_segment_type_Online <= 0.50
|   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |--- lead_time <= 163.50
|   |   |   |   |   |   |--- lead_time <= 162.50
|   |   |   |   |   |   |   |--- no_of_week_nights <= 2.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 5.00] class: 1
|   |   |   |   |   |   |   |--- no_of_week_nights >  2.50
|   |   |   |   |   |   |   |   |--- weights: [1.00, 2.00] class: 1
|   |   |   |   |   |   |--- lead_time >  162.50
|   |   |   |   |   |   |   |--- weights: [19.00, 0.00] class: 0
|   |   |   |   |   |--- lead_time >  163.50
|   |   |   |   |   |   |--- lead_time <= 341.00
|   |   |   |   |   |   |   |--- lead_time <= 173.00
|   |   |   |   |   |   |   |   |--- arrival_date <= 3.50
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 2.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [6.00, 65.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  2.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |--- arrival_date >  3.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 5.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1
|   |   |   |   |   |   |   |   |   |--- arrival_month >  5.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [10.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- lead_time >  173.00
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 98.00
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 5.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 7.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  7.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 10.00] class: 1
|   |   |   |   |   |   |   |   |   |--- arrival_month >  5.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 55.21
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  55.21
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  98.00
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 13.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |   |--- arrival_date >  13.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [4.00, 0.00] class: 0
|   |   |   |   |   |   |--- lead_time >  341.00
|   |   |   |   |   |   |   |--- no_of_week_nights <= 4.00
|   |   |   |   |   |   |   |   |--- arrival_date <= 24.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 80.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 5.00] class: 1
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  80.00
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 9.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [3.00, 5.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  9.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |--- arrival_date >  24.50
|   |   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- no_of_week_nights >  4.00
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 88.33
|   |   |   |   |   |   |   |   |   |--- weights: [8.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  88.33
|   |   |   |   |   |   |   |   |   |--- weights: [1.00, 1.00] class: 0
|   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |--- avg_price_per_room <= 84.58
|   |   |   |   |   |   |--- lead_time <= 244.00
|   |   |   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 166.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1
|   |   |   |   |   |   |   |   |   |--- lead_time >  166.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 69.34
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  69.34
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 26.00] class: 1
|   |   |   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 66.50
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 16.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [8.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  16.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 28.57
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  28.57
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  66.50
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 1 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 1 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 75.75
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  75.75
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |--- lead_time >  244.00
|   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |--- arrival_year <= 2017.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 37.00] class: 1
|   |   |   |   |   |   |   |   |--- arrival_year >  2017.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 76.00
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 3.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  3.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  76.00
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 19.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  19.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 21.00] class: 1
|   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 38.00] class: 1
|   |   |   |   |   |--- avg_price_per_room >  84.58
|   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 1 <= 0.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 4.00] class: 1
|   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 1 >  0.50
|   |   |   |   |   |   |   |   |   |--- market_segment_type_Offline <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |   |--- market_segment_type_Offline >  0.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_adults <= 2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |   |--- no_of_adults >  2.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 6.50
|   |   |   |   |   |   |   |   |   |--- weights: [15.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_month >  6.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 16.00] class: 1
|   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |--- weights: [0.00, 9.00] class: 1
|   |   |   |--- market_segment_type_Online >  0.50
|   |   |   |   |--- avg_price_per_room <= 35.17
|   |   |   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |   |   |--- arrival_date <= 12.50
|   |   |   |   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |--- arrival_date >  12.50
|   |   |   |   |   |   |   |--- arrival_date <= 19.00
|   |   |   |   |   |   |   |   |--- weights: [4.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- arrival_date >  19.00
|   |   |   |   |   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |   |   |--- weights: [0.00, 9.00] class: 1
|   |   |   |   |--- avg_price_per_room >  35.17
|   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |--- weights: [561.00, 0.00] class: 0
|   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |   |--- arrival_date <= 3.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |--- arrival_date >  3.50
|   |   |   |   |   |   |   |   |--- lead_time <= 214.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |--- lead_time >  214.50
|   |   |   |   |   |   |   |   |   |--- weights: [7.00, 0.00] class: 0
|   |   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 76.87
|   |   |   |   |   |   |   |   |   |--- weights: [9.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  76.87
|   |   |   |   |   |   |   |   |   |--- lead_time <= 270.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1
|   |   |   |   |   |   |   |   |   |--- lead_time >  270.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |   |   |   |   |--- weights: [60.00, 0.00] class: 0
|   |   |--- no_of_special_requests >  0.50
|   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |--- lead_time <= 180.50
|   |   |   |   |   |--- lead_time <= 159.50
|   |   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 98.81
|   |   |   |   |   |   |   |   |--- weights: [0.00, 9.00] class: 1
|   |   |   |   |   |   |   |--- avg_price_per_room >  98.81
|   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |   |--- arrival_date <= 23.50
|   |   |   |   |   |   |   |   |--- lead_time <= 156.50
|   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- lead_time >  156.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |--- arrival_date >  23.50
|   |   |   |   |   |   |   |   |--- weights: [4.00, 0.00] class: 0
|   |   |   |   |   |--- lead_time >  159.50
|   |   |   |   |   |   |--- arrival_date <= 1.50
|   |   |   |   |   |   |   |--- lead_time <= 176.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1
|   |   |   |   |   |   |   |--- lead_time >  176.50
|   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |--- arrival_date >  1.50
|   |   |   |   |   |   |   |--- no_of_adults <= 0.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 96.08
|   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  96.08
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |--- no_of_adults >  0.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 57.00] class: 1
|   |   |   |   |--- lead_time >  180.50
|   |   |   |   |   |--- market_segment_type_Online <= 0.50
|   |   |   |   |   |   |--- no_of_adults <= 2.50
|   |   |   |   |   |   |   |--- lead_time <= 356.00
|   |   |   |   |   |   |   |   |--- lead_time <= 302.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 16.00] class: 1
|   |   |   |   |   |   |   |   |--- lead_time >  302.50
|   |   |   |   |   |   |   |   |   |--- no_of_special_requests <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 2.00] class: 1
|   |   |   |   |   |   |   |   |   |--- no_of_special_requests >  1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |--- lead_time >  356.00
|   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |--- no_of_adults >  2.50
|   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |--- market_segment_type_Online >  0.50
|   |   |   |   |   |   |--- no_of_special_requests <= 2.50
|   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 44.12
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  44.12
|   |   |   |   |   |   |   |   |   |--- weights: [137.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |--- lead_time <= 300.00
|   |   |   |   |   |   |   |   |   |--- lead_time <= 221.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- lead_time >  221.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 272.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 8.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  272.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |--- lead_time >  300.00
|   |   |   |   |   |   |   |   |   |--- weights: [6.00, 0.00] class: 0
|   |   |   |   |   |   |--- no_of_special_requests >  2.50
|   |   |   |   |   |   |   |--- weights: [0.00, 12.00] class: 1
|   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |--- market_segment_type_Online <= 0.50
|   |   |   |   |   |--- lead_time <= 348.50
|   |   |   |   |   |   |--- no_of_week_nights <= 5.50
|   |   |   |   |   |   |   |--- arrival_date <= 30.00
|   |   |   |   |   |   |   |   |--- weights: [0.00, 151.00] class: 1
|   |   |   |   |   |   |   |--- arrival_date >  30.00
|   |   |   |   |   |   |   |   |--- no_of_week_nights <= 3.00
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1
|   |   |   |   |   |   |   |   |--- no_of_week_nights >  3.00
|   |   |   |   |   |   |   |   |   |--- weights: [1.00, 2.00] class: 1
|   |   |   |   |   |   |--- no_of_week_nights >  5.50
|   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 1 <= 0.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 1 >  0.50
|   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |--- lead_time >  348.50
|   |   |   |   |   |   |--- avg_price_per_room <= 58.50
|   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |--- avg_price_per_room >  58.50
|   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 <= 0.50
|   |   |   |   |   |   |   |   |--- weights: [2.00, 6.00] class: 1
|   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 >  0.50
|   |   |   |   |   |   |   |   |--- weights: [1.00, 2.00] class: 1
|   |   |   |   |--- market_segment_type_Online >  0.50
|   |   |   |   |   |--- no_of_week_nights <= 9.50
|   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |--- arrival_date <= 27.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 81.12
|   |   |   |   |   |   |   |   |   |--- lead_time <= 157.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 4.00] class: 1
|   |   |   |   |   |   |   |   |   |--- lead_time >  157.50
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 65.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  81.12
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 6.50
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 1 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 11
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 1 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 13
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  6.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 204.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  204.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- arrival_date >  27.50
|   |   |   |   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 224.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 175.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  175.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [10.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- lead_time >  224.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 4.00] class: 1
|   |   |   |   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 269.00
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 176.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  176.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |--- lead_time >  269.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0
|   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |--- arrival_date <= 14.50
|   |   |   |   |   |   |   |   |--- arrival_date <= 3.00
|   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_date >  3.00
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 64.43
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 8.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  8.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  64.43
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 9.00] class: 1
|   |   |   |   |   |   |   |--- arrival_date >  14.50
|   |   |   |   |   |   |   |   |--- no_of_week_nights <= 2.50
|   |   |   |   |   |   |   |   |   |--- no_of_special_requests <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [8.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- no_of_special_requests >  1.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 19.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  19.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |   |--- no_of_week_nights >  2.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 55.92
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  55.92
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 79.94
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  79.94
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |--- no_of_week_nights >  9.50
|   |   |   |   |   |   |--- room_type_reserved_Room_Type 2 <= 0.50
|   |   |   |   |   |   |   |--- weights: [7.00, 0.00] class: 0
|   |   |   |   |   |   |--- room_type_reserved_Room_Type 2 >  0.50
|   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |--- avg_price_per_room >  100.04
|   |   |--- arrival_month <= 11.50
|   |   |   |--- no_of_special_requests <= 2.50
|   |   |   |   |--- weights: [2245.00, 0.00] class: 0
|   |   |   |--- no_of_special_requests >  2.50
|   |   |   |   |--- weights: [0.00, 34.00] class: 1
|   |   |--- arrival_month >  11.50
|   |   |   |--- no_of_special_requests <= 0.50
|   |   |   |   |--- weights: [0.00, 51.00] class: 1
|   |   |   |--- no_of_special_requests >  0.50
|   |   |   |   |--- arrival_date <= 24.50
|   |   |   |   |   |--- weights: [0.00, 5.00] class: 1
|   |   |   |   |--- arrival_date >  24.50
|   |   |   |   |   |--- room_type_reserved_Room_Type 1 <= 0.50
|   |   |   |   |   |   |--- lead_time <= 172.50
|   |   |   |   |   |   |   |--- no_of_week_nights <= 2.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |--- no_of_week_nights >  2.50
|   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |--- lead_time >  172.50
|   |   |   |   |   |   |   |--- weights: [14.00, 0.00] class: 0
|   |   |   |   |   |--- room_type_reserved_Room_Type 1 >  0.50
|   |   |   |   |   |   |--- lead_time <= 285.00
|   |   |   |   |   |   |   |--- weights: [0.00, 4.00] class: 1
|   |   |   |   |   |   |--- lead_time >  285.00
|   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0

Observation

Features like lead_time, no_of_special_requests, and market_segment_type appear early in the tree, indicating their strong influence on the cancelation

# importance of features in the tree building ( The importance of a feature is computed as the 
#(normalized) total reduction of the criterion brought by that feature. It is also known as the Gini importance )

print (pd.DataFrame(model0.feature_importances_, columns = ["Imp"], index = X_train_1.columns).sort_values(by = 'Imp', ascending = False))

                                           Imp
lead_time                             0.358790
avg_price_per_room                    0.166805
market_segment_type_Online            0.094208
arrival_date                          0.079456
no_of_special_requests                0.068251
arrival_month                         0.065287
no_of_week_nights                     0.046546
no_of_weekend_nights                  0.040914
no_of_adults                          0.023795
arrival_year                          0.012419
type_of_meal_plan_Meal Plan 1         0.008264
required_car_parking_space            0.007264
room_type_reserved_Room_Type 1        0.006579
room_type_reserved_Room_Type 4        0.004439
no_of_children                        0.004106
type_of_meal_plan_Not Selected        0.002898
type_of_meal_plan_Meal Plan 2         0.002819
market_segment_type_Offline           0.001660
room_type_reserved_Room_Type 2        0.001513
room_type_reserved_Room_Type 5        0.000896
market_segment_type_Corporate         0.000659
no_of_previous_bookings_not_canceled  0.000650
market_segment_type_Aviation          0.000407
room_type_reserved_Room_Type 7        0.000381
no_of_previous_cancellations          0.000379
repeated_guest                        0.000319
room_type_reserved_Room_Type 6        0.000294
room_type_reserved_Room_Type 3        0.000000
type_of_meal_plan_Meal Plan 3         0.000000
market_segment_type_Complementary     0.000000

importances = model0.feature_importances_
indices = np.argsort(importances)

plt.figure(figsize=(12,12))
plt.title('Feature Importances')
plt.barh(range(len(indices)), importances[indices], color='violet', align='center')
plt.yticks(range(len(indices)), [feature_name[i] for i in indices])
plt.xlabel('Relative Importance')
plt.show()

Decision Tree (with class_weights)

Train data-set

model1=DecisionTreeClassifier(random_state=1,class_weight="balanced")
model1.fit(X_train_1,y_train_1)

DecisionTreeClassifier(class_weight='balanced', random_state=1)

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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decision_tree_perf_train_with_weigth= model_performance_classification_sklearn(model1,X_train_1,y_train_1)
decision_tree_perf_train_with_weigth

	Accuracy	Recall	Precision	F1
0	0.992832	0.991794	0.997524	0.994651

confusion_matrix_sklearn(model1,X_test_1,y_test_1)

Test dataset with weigth

decision_tree_perf_test_with_weigth= model_performance_classification_sklearn(model1,X_test_1,y_test_1)
decision_tree_perf_test_with_weigth

	Accuracy	Recall	Precision	F1
0	0.86294	0.890362	0.904722	0.897485

confusion_matrix_sklearn(model1,X_test_1,y_test_1)

• There is a huge disparity in performance of model on training set and test set, which suggests that the model is overfiiting.

plt.figure(figsize=(15,10))

tree.plot_tree(model1,feature_names=feature_name,filled=True,fontsize=9,node_ids=True)
plt.show()

print(tree.export_text(model1,feature_names=feature_name,show_weights=True))

|--- lead_time <= 151.50
|   |--- no_of_special_requests <= 0.50
|   |   |--- market_segment_type_Online <= 0.50
|   |   |   |--- lead_time <= 90.50
|   |   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |   |--- avg_price_per_room <= 179.47
|   |   |   |   |   |   |--- market_segment_type_Offline <= 0.50
|   |   |   |   |   |   |   |--- lead_time <= 16.50
|   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |   |   |   |--- repeated_guest <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 162.09
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 13
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  162.09
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- repeated_guest >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 113.86] class: 1
|   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 83.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 11.91] class: 1
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  83.00
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 9.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  9.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |--- lead_time >  16.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 135.00
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 119.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 14
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  119.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 23.81] class: 1
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  135.00
|   |   |   |   |   |   |   |   |   |--- weights: [12.19, 0.00] class: 0
|   |   |   |   |   |   |--- market_segment_type_Offline >  0.50
|   |   |   |   |   |   |   |--- weights: [0.00, 1285.96] class: 1
|   |   |   |   |   |--- avg_price_per_room >  179.47
|   |   |   |   |   |   |--- lead_time <= 22.00
|   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |   |   |--- arrival_date <= 23.00
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.74] class: 1
|   |   |   |   |   |   |   |   |--- arrival_date >  23.00
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.49] class: 1
|   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |   |   |--- weights: [1.52, 0.00] class: 0
|   |   |   |   |   |   |--- lead_time >  22.00
|   |   |   |   |   |   |   |--- lead_time <= 37.50
|   |   |   |   |   |   |   |   |--- weights: [27.43, 0.00] class: 0
|   |   |   |   |   |   |   |--- lead_time >  37.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 0.74] class: 1
|   |   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |   |--- lead_time <= 68.50
|   |   |   |   |   |   |--- arrival_month <= 9.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 63.29
|   |   |   |   |   |   |   |   |--- arrival_date <= 20.50
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 45.40] class: 1
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  2.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.52, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_date >  20.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 59.75
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 23.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  23.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  59.75
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 44.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  44.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |--- avg_price_per_room >  63.29
|   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 3.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 59.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 7.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 11
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  7.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10
|   |   |   |   |   |   |   |   |   |--- lead_time >  59.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 5.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  5.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 22.33] class: 1
|   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  3.50
|   |   |   |   |   |   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [16.76, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.74] class: 1
|   |   |   |   |   |   |--- arrival_month >  9.50
|   |   |   |   |   |   |   |--- market_segment_type_Aviation <= 0.50
|   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 5.00
|   |   |   |   |   |   |   |   |   |--- lead_time <= 65.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 64.90
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 93.77] class: 1
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  64.90
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10
|   |   |   |   |   |   |   |   |   |--- lead_time >  65.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 10.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  10.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 8.19] class: 1
|   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  5.00
|   |   |   |   |   |   |   |   |   |--- weights: [1.52, 0.00] class: 0
|   |   |   |   |   |   |   |--- market_segment_type_Aviation >  0.50
|   |   |   |   |   |   |   |   |--- arrival_date <= 7.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 5.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.74] class: 1
|   |   |   |   |   |   |   |   |   |--- arrival_date >  5.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [4.57, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_date >  7.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 20.00
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.49] class: 1
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 8.93] class: 1
|   |   |   |   |   |   |   |   |   |--- lead_time >  20.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.52, 0.00] class: 0
|   |   |   |   |   |--- lead_time >  68.50
|   |   |   |   |   |   |--- avg_price_per_room <= 99.98
|   |   |   |   |   |   |   |--- arrival_month <= 3.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 62.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 15.63] class: 1
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  62.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 80.38
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 81.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  81.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.23] class: 1
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  80.38
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.72] class: 1
|   |   |   |   |   |   |   |--- arrival_month >  3.50
|   |   |   |   |   |   |   |   |--- lead_time <= 71.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 8.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [4.57, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  8.00
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.49] class: 1
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.74] class: 1
|   |   |   |   |   |   |   |   |--- lead_time >  71.50
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 25.30] class: 1
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 87.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  87.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |--- avg_price_per_room >  99.98
|   |   |   |   |   |   |   |--- lead_time <= 81.00
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 123.25
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 6 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [80.76, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 6 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.74] class: 1
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  123.25
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 7.44] class: 1
|   |   |   |   |   |   |   |--- lead_time >  81.00
|   |   |   |   |   |   |   |   |--- lead_time <= 88.50
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 4.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 12.65] class: 1
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  4.50
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 1 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.52, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 1 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.74] class: 1
|   |   |   |   |   |   |   |   |--- lead_time >  88.50
|   |   |   |   |   |   |   |   |   |--- weights: [3.05, 0.00] class: 0
|   |   |   |--- lead_time >  90.50
|   |   |   |   |--- lead_time <= 117.50
|   |   |   |   |   |--- avg_price_per_room <= 93.58
|   |   |   |   |   |   |--- avg_price_per_room <= 75.07
|   |   |   |   |   |   |   |--- no_of_week_nights <= 2.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 58.75
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 6.70] class: 1
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  58.75
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 7.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_year <= 2017.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.49] class: 1
|   |   |   |   |   |   |   |   |   |   |--- arrival_year >  2017.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |--- arrival_month >  7.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 9.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  9.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |--- no_of_week_nights >  2.50
|   |   |   |   |   |   |   |   |--- arrival_date <= 23.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 11.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.98] class: 1
|   |   |   |   |   |   |   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 31.26] class: 1
|   |   |   |   |   |   |   |   |   |--- arrival_date >  11.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 13.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  13.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 8.93] class: 1
|   |   |   |   |   |   |   |   |--- arrival_date >  23.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 9.00
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 73.62
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  73.62
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.98] class: 1
|   |   |   |   |   |   |   |   |   |--- arrival_month >  9.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 8.93] class: 1
|   |   |   |   |   |   |--- avg_price_per_room >  75.07
|   |   |   |   |   |   |   |--- arrival_month <= 3.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 88.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 102.00
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 80.25
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  80.25
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 24.56] class: 1
|   |   |   |   |   |   |   |   |   |--- lead_time >  102.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 33.49] class: 1
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  88.50
|   |   |   |   |   |   |   |   |   |--- market_segment_type_Offline <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.52, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- market_segment_type_Offline >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.74] class: 1
|   |   |   |   |   |   |   |--- arrival_month >  3.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 4.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 80.38
|   |   |   |   |   |   |   |   |   |   |--- weights: [18.29, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  80.38
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.74] class: 1
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.74] class: 1
|   |   |   |   |   |   |   |   |--- arrival_month >  4.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 15.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 79.28
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  79.28
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |--- arrival_date >  15.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 85.38
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  85.38
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |--- avg_price_per_room >  93.58
|   |   |   |   |   |   |--- arrival_date <= 11.50
|   |   |   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |   |   |--- lead_time <= 110.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 5.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.52, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  5.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_children <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- no_of_children >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.52, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- lead_time >  110.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 3.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 116.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [12.19, 4.47] class: 0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  116.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.74] class: 1
|   |   |   |   |   |   |   |   |   |--- arrival_date >  3.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [15.24, 0.00] class: 0
|   |   |   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 125.00
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 95.83
|   |   |   |   |   |   |   |   |   |   |--- no_of_children <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.52, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- no_of_children >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.74] class: 1
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  95.83
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 112.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  112.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  125.00
|   |   |   |   |   |   |   |   |   |--- weights: [1.52, 0.00] class: 0
|   |   |   |   |   |   |--- arrival_date >  11.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 102.09
|   |   |   |   |   |   |   |   |--- arrival_date <= 14.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.49] class: 1
|   |   |   |   |   |   |   |   |--- arrival_date >  14.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 2.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.74] class: 1
|   |   |   |   |   |   |   |   |   |--- arrival_month >  2.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 95.44
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  95.44
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |--- avg_price_per_room >  102.09
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 109.50
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 108.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [16.76, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  108.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.74] class: 1
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 18.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 32.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  18.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.72] class: 1
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  109.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 124.25
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 19.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [74.67, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  19.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  124.25
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.98] class: 1
|   |   |   |   |--- lead_time >  117.50
|   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |--- arrival_date <= 7.50
|   |   |   |   |   |   |   |--- weights: [0.00, 38.70] class: 1
|   |   |   |   |   |   |--- arrival_date >  7.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 93.58
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 65.38
|   |   |   |   |   |   |   |   |   |--- weights: [4.57, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  65.38
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 89.88
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 18.60] class: 1
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  89.88
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 10.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [3.05, 5.95] class: 1
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  10.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.74] class: 1
|   |   |   |   |   |   |   |--- avg_price_per_room >  93.58
|   |   |   |   |   |   |   |   |--- arrival_date <= 28.00
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 1 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 24.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  24.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.74] class: 1
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 1 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_adults <= 2.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [28.95, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- no_of_adults >  2.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.52, 0.74] class: 0
|   |   |   |   |   |   |   |   |--- arrival_date >  28.00
|   |   |   |   |   |   |   |   |   |--- weights: [3.05, 10.42] class: 1
|   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |   |   |--- arrival_date <= 26.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 83.35] class: 1
|   |   |   |   |   |   |   |--- arrival_date >  26.50
|   |   |   |   |   |   |   |   |--- arrival_date <= 28.50
|   |   |   |   |   |   |   |   |   |--- weights: [1.52, 1.49] class: 0
|   |   |   |   |   |   |   |   |--- arrival_date >  28.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 74.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.74] class: 1
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  74.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.49] class: 1
|   |   |   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |   |   |--- lead_time <= 125.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 90.85
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 87.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 14.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.23] class: 1
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  14.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  87.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [15.24, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  90.85
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 11.16] class: 1
|   |   |   |   |   |   |   |--- lead_time >  125.50
|   |   |   |   |   |   |   |   |--- arrival_date <= 19.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 10.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 155.78
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  155.78
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.52, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_date >  10.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 10.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  10.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [3.05, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_date >  19.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 128.00
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 87.08
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.23] class: 1
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  87.08
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.52, 4.47] class: 1
|   |   |   |   |   |   |   |   |   |--- lead_time >  128.00
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 59.53] class: 1
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.74] class: 1
|   |   |--- market_segment_type_Online >  0.50
|   |   |   |--- lead_time <= 9.50
|   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |--- lead_time <= 3.50
|   |   |   |   |   |   |--- avg_price_per_room <= 134.50
|   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 1.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 46.88] class: 1
|   |   |   |   |   |   |   |   |--- arrival_month >  1.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 2.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |--- arrival_month >  2.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_year <= 2017.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |   |--- arrival_year >  2017.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 13
|   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |   |   |   |--- arrival_date <= 27.00
|   |   |   |   |   |   |   |   |   |--- arrival_year <= 2017.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 76.75
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [3.05, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  76.75
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- arrival_year >  2017.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 2.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 20.09] class: 1
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |--- arrival_date >  27.00
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 6.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.74] class: 1
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [19.81, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  6.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 92.32
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.74] class: 1
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  92.32
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.74] class: 1
|   |   |   |   |   |   |--- avg_price_per_room >  134.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 136.09
|   |   |   |   |   |   |   |   |--- arrival_date <= 20.00
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.74] class: 1
|   |   |   |   |   |   |   |   |--- arrival_date >  20.00
|   |   |   |   |   |   |   |   |   |--- weights: [9.14, 0.00] class: 0
|   |   |   |   |   |   |   |--- avg_price_per_room >  136.09
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 178.78
|   |   |   |   |   |   |   |   |   |--- lead_time <= 2.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 4.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  4.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.52, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- lead_time >  2.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_children <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- no_of_children >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  178.78
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 3.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.23] class: 1
|   |   |   |   |   |   |   |   |   |--- arrival_month >  3.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_adults <= 2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- no_of_adults >  2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |--- lead_time >  3.50
|   |   |   |   |   |   |--- avg_price_per_room <= 99.38
|   |   |   |   |   |   |   |--- arrival_month <= 1.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 28.28] class: 1
|   |   |   |   |   |   |   |--- arrival_month >  1.50
|   |   |   |   |   |   |   |   |--- arrival_date <= 3.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 10.42] class: 1
|   |   |   |   |   |   |   |   |--- arrival_date >  3.50
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 20.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  20.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 78.05
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.98] class: 1
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  78.05
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |--- avg_price_per_room >  99.38
|   |   |   |   |   |   |   |--- arrival_date <= 23.50
|   |   |   |   |   |   |   |   |--- required_car_parking_space <= 0.50
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 5 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 12
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 5 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.74] class: 1
|   |   |   |   |   |   |   |   |--- required_car_parking_space >  0.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 172.78
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.98] class: 1
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  172.78
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.52, 0.00] class: 0
|   |   |   |   |   |   |   |--- arrival_date >  23.50
|   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 5.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 24.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  24.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 11.91] class: 1
|   |   |   |   |   |   |   |   |   |--- arrival_month >  5.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 7.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [7.62, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  7.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 24.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.74] class: 1
|   |   |   |   |   |   |   |   |   |--- arrival_date >  24.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [13.71, 0.00] class: 0
|   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |--- avg_price_per_room <= 167.00
|   |   |   |   |   |   |--- arrival_year <= 2017.50
|   |   |   |   |   |   |   |--- arrival_month <= 9.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 117.30
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 17.86] class: 1
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  117.30
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 120.07
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.52, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  120.07
|   |   |   |   |   |   |   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 11.91] class: 1
|   |   |   |   |   |   |   |--- arrival_month >  9.50
|   |   |   |   |   |   |   |   |--- no_of_previous_cancellations <= 0.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 114.60] class: 1
|   |   |   |   |   |   |   |   |--- no_of_previous_cancellations >  0.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.49] class: 1
|   |   |   |   |   |   |--- arrival_year >  2017.50
|   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |--- lead_time <= 1.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 28.00
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 33.49] class: 1
|   |   |   |   |   |   |   |   |   |--- arrival_date >  28.00
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 1 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 1 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.72] class: 1
|   |   |   |   |   |   |   |   |--- lead_time >  1.50
|   |   |   |   |   |   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 8.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  8.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 24.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  24.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 5.95] class: 1
|   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 43.16] class: 1
|   |   |   |   |   |--- avg_price_per_room >  167.00
|   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |--- arrival_date <= 15.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 176.17
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [3.05, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_year <= 2017.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.74] class: 1
|   |   |   |   |   |   |   |   |   |   |--- arrival_year >  2017.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.49] class: 1
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  176.17
|   |   |   |   |   |   |   |   |   |--- weights: [10.67, 0.00] class: 0
|   |   |   |   |   |   |   |--- arrival_date >  15.50
|   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 6 <= 0.50
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 1 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.72] class: 1
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 1 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.49] class: 1
|   |   |   |   |   |   |   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.72] class: 1
|   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 6 >  0.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 178.28
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.49] class: 1
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  178.28
|   |   |   |   |   |   |   |   |   |   |--- weights: [6.10, 0.00] class: 0
|   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |--- weights: [0.00, 4.47] class: 1
|   |   |   |--- lead_time >  9.50
|   |   |   |   |--- avg_price_per_room <= 105.27
|   |   |   |   |   |--- lead_time <= 25.50
|   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |--- arrival_month <= 1.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 40.19] class: 1
|   |   |   |   |   |   |   |--- arrival_month >  1.50
|   |   |   |   |   |   |   |   |--- arrival_year <= 2017.50
|   |   |   |   |   |   |   |   |   |--- no_of_adults <= 2.50
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 26.79] class: 1
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- no_of_adults >  2.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [3.05, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_year >  2017.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 34.62
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.72] class: 1
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  34.62
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 13.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  13.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 19
|   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |--- weights: [0.00, 73.67] class: 1
|   |   |   |   |   |--- lead_time >  25.50
|   |   |   |   |   |   |--- avg_price_per_room <= 60.19
|   |   |   |   |   |   |   |--- lead_time <= 84.50
|   |   |   |   |   |   |   |   |--- arrival_date <= 13.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 54.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 36.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.23] class: 1
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  36.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |--- lead_time >  54.50
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 5.21] class: 1
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 6.70] class: 1
|   |   |   |   |   |   |   |   |--- arrival_date >  13.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 26.79] class: 1
|   |   |   |   |   |   |   |--- lead_time >  84.50
|   |   |   |   |   |   |   |   |--- arrival_year <= 2017.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 27.00
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 131.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  131.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- arrival_date >  27.00
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 2.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.74] class: 1
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  2.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.98] class: 1
|   |   |   |   |   |   |   |   |--- arrival_year >  2017.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 59.43
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 11.91] class: 1
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  59.43
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.52, 0.00] class: 0
|   |   |   |   |   |   |--- avg_price_per_room >  60.19
|   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 1 <= 0.50
|   |   |   |   |   |   |   |   |--- required_car_parking_space <= 0.50
|   |   |   |   |   |   |   |   |   |--- no_of_children <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 87.97
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 17
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  87.97
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 17
|   |   |   |   |   |   |   |   |   |--- no_of_children >  0.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 11.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.98] class: 1
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  11.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.52, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- required_car_parking_space >  0.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 4.47] class: 1
|   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 1 >  0.50
|   |   |   |   |   |   |   |   |--- arrival_year <= 2017.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 58.00
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 27.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  27.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |--- lead_time >  58.00
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 80.72
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 14
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  80.72
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |--- arrival_year >  2017.50
|   |   |   |   |   |   |   |   |   |--- required_car_parking_space <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 16
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 26
|   |   |   |   |   |   |   |   |   |--- required_car_parking_space >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 8.93] class: 1
|   |   |   |   |--- avg_price_per_room >  105.27
|   |   |   |   |   |--- required_car_parking_space <= 0.50
|   |   |   |   |   |   |--- arrival_year <= 2017.50
|   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 <= 0.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 115.66
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 14.14] class: 1
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  115.66
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 116.97
|   |   |   |   |   |   |   |   |   |   |--- weights: [3.05, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  116.97
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 145.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  145.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 7.44] class: 1
|   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 >  0.50
|   |   |   |   |   |   |   |   |--- weights: [19.81, 0.00] class: 0
|   |   |   |   |   |   |--- arrival_year >  2017.50
|   |   |   |   |   |   |   |--- arrival_month <= 10.50
|   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 5 <= 0.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 144.76
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 23.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 24
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  23.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 17
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  144.76
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 20
|   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 5 >  0.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 26.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 175.71
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  175.71
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [3.05, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_date >  26.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [7.62, 0.00] class: 0
|   |   |   |   |   |   |   |--- arrival_month >  10.50
|   |   |   |   |   |   |   |   |--- lead_time <= 46.00
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 39.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  39.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 24.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 15.63] class: 1
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  24.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |--- lead_time >  46.00
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 3.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 175.65
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  175.65
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  3.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.23] class: 1
|   |   |   |   |   |--- required_car_parking_space >  0.50
|   |   |   |   |   |   |--- no_of_weekend_nights <= 3.00
|   |   |   |   |   |   |   |--- lead_time <= 13.50
|   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.00
|   |   |   |   |   |   |   |   |   |--- weights: [1.52, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.00
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.74] class: 1
|   |   |   |   |   |   |   |--- lead_time >  13.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 34.23] class: 1
|   |   |   |   |   |   |--- no_of_weekend_nights >  3.00
|   |   |   |   |   |   |   |--- weights: [1.52, 0.00] class: 0
|   |--- no_of_special_requests >  0.50
|   |   |--- no_of_special_requests <= 1.50
|   |   |   |--- market_segment_type_Online <= 0.50
|   |   |   |   |--- lead_time <= 102.50
|   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |--- no_of_week_nights <= 11.00
|   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 5 <= 0.50
|   |   |   |   |   |   |   |   |--- lead_time <= 91.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 129.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 672.00] class: 1
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  129.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 131.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  131.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 21.58] class: 1
|   |   |   |   |   |   |   |   |--- lead_time >  91.50
|   |   |   |   |   |   |   |   |   |--- no_of_children <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 32.74] class: 1
|   |   |   |   |   |   |   |   |   |--- no_of_children >  0.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 95.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [3.05, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  95.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.49] class: 1
|   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 5 >  0.50
|   |   |   |   |   |   |   |   |--- market_segment_type_Corporate <= 0.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 8.19] class: 1
|   |   |   |   |   |   |   |   |--- market_segment_type_Corporate >  0.50
|   |   |   |   |   |   |   |   |   |--- repeated_guest <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [3.05, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- repeated_guest >  0.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 6.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.74] class: 1
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  6.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.74] class: 1
|   |   |   |   |   |   |--- no_of_week_nights >  11.00
|   |   |   |   |   |   |   |--- weights: [1.52, 0.00] class: 0
|   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |--- lead_time <= 63.00
|   |   |   |   |   |   |   |--- market_segment_type_Corporate <= 0.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 13.40] class: 1
|   |   |   |   |   |   |   |--- market_segment_type_Corporate >  0.50
|   |   |   |   |   |   |   |   |--- arrival_date <= 14.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.74] class: 1
|   |   |   |   |   |   |   |   |--- arrival_date >  14.50
|   |   |   |   |   |   |   |   |   |--- weights: [1.52, 1.49] class: 0
|   |   |   |   |   |   |--- lead_time >  63.00
|   |   |   |   |   |   |   |--- weights: [7.62, 0.00] class: 0
|   |   |   |   |--- lead_time >  102.50
|   |   |   |   |   |--- no_of_week_nights <= 2.50
|   |   |   |   |   |   |--- lead_time <= 105.00
|   |   |   |   |   |   |   |--- avg_price_per_room <= 67.65
|   |   |   |   |   |   |   |   |--- weights: [0.00, 0.74] class: 1
|   |   |   |   |   |   |   |--- avg_price_per_room >  67.65
|   |   |   |   |   |   |   |   |--- weights: [6.10, 0.00] class: 0
|   |   |   |   |   |   |--- lead_time >  105.00
|   |   |   |   |   |   |   |--- avg_price_per_room <= 83.39
|   |   |   |   |   |   |   |   |--- arrival_month <= 3.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 4.47] class: 1
|   |   |   |   |   |   |   |   |--- arrival_month >  3.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 6.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.23] class: 1
|   |   |   |   |   |   |   |   |   |--- arrival_date >  6.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 4.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  4.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [7.62, 0.00] class: 0
|   |   |   |   |   |   |   |--- avg_price_per_room >  83.39
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 141.25
|   |   |   |   |   |   |   |   |   |--- lead_time <= 143.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 25.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 17.86] class: 1
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  25.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- lead_time >  143.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 5.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.52, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  5.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  141.25
|   |   |   |   |   |   |   |   |   |--- weights: [1.52, 0.00] class: 0
|   |   |   |   |   |--- no_of_week_nights >  2.50
|   |   |   |   |   |   |--- avg_price_per_room <= 122.00
|   |   |   |   |   |   |   |--- avg_price_per_room <= 97.33
|   |   |   |   |   |   |   |   |--- weights: [0.00, 35.72] class: 1
|   |   |   |   |   |   |   |--- avg_price_per_room >  97.33
|   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 <= 0.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 102.28
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.74] class: 1
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  102.28
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 8.19] class: 1
|   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 >  0.50
|   |   |   |   |   |   |   |   |   |--- weights: [1.52, 0.74] class: 0
|   |   |   |   |   |   |--- avg_price_per_room >  122.00
|   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 1 <= 0.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 1.49] class: 1
|   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 1 >  0.50
|   |   |   |   |   |   |   |   |--- no_of_children <= 0.50
|   |   |   |   |   |   |   |   |   |--- weights: [3.05, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- no_of_children >  0.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.74] class: 1
|   |   |   |--- market_segment_type_Online >  0.50
|   |   |   |   |--- lead_time <= 6.50
|   |   |   |   |   |--- no_of_week_nights <= 10.00
|   |   |   |   |   |   |--- avg_price_per_room <= 157.93
|   |   |   |   |   |   |   |--- arrival_date <= 13.50
|   |   |   |   |   |   |   |   |--- lead_time <= 4.50
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 2 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 10.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 11
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  10.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 36.47] class: 1
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 2 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.23] class: 1
|   |   |   |   |   |   |   |   |--- lead_time >  4.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 9.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 87.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  87.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |   |   |--- arrival_month >  9.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 17.12] class: 1
|   |   |   |   |   |   |   |--- arrival_date >  13.50
|   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 19.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |--- arrival_date >  19.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 87.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  87.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 22.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 14.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.72] class: 1
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  14.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 58.05] class: 1
|   |   |   |   |   |   |   |   |   |--- arrival_date >  22.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 131.67
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  131.67
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |--- avg_price_per_room >  157.93
|   |   |   |   |   |   |   |--- arrival_date <= 18.50
|   |   |   |   |   |   |   |   |--- arrival_date <= 16.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 4.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 179.28
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 20.84] class: 1
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  179.28
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |   |--- lead_time >  4.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 165.88
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  165.88
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |--- arrival_date >  16.50
|   |   |   |   |   |   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.74] class: 1
|   |   |   |   |   |   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 6 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 6 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.74] class: 1
|   |   |   |   |   |   |   |--- arrival_date >  18.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 5.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 20.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.49] class: 1
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  20.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |--- lead_time >  1.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 2.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 5.95] class: 1
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  2.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.98] class: 1
|   |   |   |   |   |   |   |   |--- arrival_month >  5.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 29.02] class: 1
|   |   |   |   |   |--- no_of_week_nights >  10.00
|   |   |   |   |   |   |--- lead_time <= 4.50
|   |   |   |   |   |   |   |--- weights: [3.05, 0.00] class: 0
|   |   |   |   |   |   |--- lead_time >  4.50
|   |   |   |   |   |   |   |--- weights: [0.00, 0.74] class: 1
|   |   |   |   |--- lead_time >  6.50
|   |   |   |   |   |--- required_car_parking_space <= 0.50
|   |   |   |   |   |   |--- avg_price_per_room <= 118.64
|   |   |   |   |   |   |   |--- lead_time <= 59.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 4.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 74.42] class: 1
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 20
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  4.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 6.70] class: 1
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 8.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 141.40] class: 1
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  8.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 79.30
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.52, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  79.30
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.52, 0.00] class: 0
|   |   |   |   |   |   |   |--- lead_time >  59.50
|   |   |   |   |   |   |   |   |--- arrival_year <= 2017.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 7.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_children <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- no_of_children >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- arrival_month >  7.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 66.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 12.65] class: 1
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  66.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |   |--- arrival_year >  2017.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 9.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 71.93
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  71.93
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 17
|   |   |   |   |   |   |   |   |   |--- arrival_month >  9.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 14
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10
|   |   |   |   |   |   |--- avg_price_per_room >  118.64
|   |   |   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |   |   |--- arrival_date <= 19.50
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 7.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 177.15
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 15
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  177.15
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  7.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [6.10, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_date >  19.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 27.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 126.95
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  126.95
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 13
|   |   |   |   |   |   |   |   |   |--- arrival_date >  27.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 55.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  55.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |   |   |--- arrival_year <= 2017.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 9.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 28.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  28.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 4.47] class: 1
|   |   |   |   |   |   |   |   |   |--- arrival_month >  9.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 42.42] class: 1
|   |   |   |   |   |   |   |   |--- arrival_year >  2017.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 7 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 30
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 7 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 100.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 55.07] class: 1
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  100.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |--- required_car_parking_space >  0.50
|   |   |   |   |   |   |--- lead_time <= 150.00
|   |   |   |   |   |   |   |--- no_of_week_nights <= 7.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 150.33] class: 1
|   |   |   |   |   |   |   |--- no_of_week_nights >  7.50
|   |   |   |   |   |   |   |   |--- weights: [1.52, 0.00] class: 0
|   |   |   |   |   |   |--- lead_time >  150.00
|   |   |   |   |   |   |   |--- weights: [1.52, 0.00] class: 0
|   |   |--- no_of_special_requests >  1.50
|   |   |   |--- lead_time <= 90.50
|   |   |   |   |--- no_of_week_nights <= 3.50
|   |   |   |   |   |--- required_car_parking_space <= 0.50
|   |   |   |   |   |   |--- weights: [0.00, 1597.77] class: 1
|   |   |   |   |   |--- required_car_parking_space >  0.50
|   |   |   |   |   |   |--- weights: [0.00, 109.40] class: 1
|   |   |   |   |--- no_of_week_nights >  3.50
|   |   |   |   |   |--- no_of_week_nights <= 9.50
|   |   |   |   |   |   |--- no_of_special_requests <= 2.50
|   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |--- arrival_date <= 5.50
|   |   |   |   |   |   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 1 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.52, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 1 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.74] class: 1
|   |   |   |   |   |   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 27.53] class: 1
|   |   |   |   |   |   |   |   |--- arrival_date >  5.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 8.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_children <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 24.56] class: 1
|   |   |   |   |   |   |   |   |   |   |--- no_of_children >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |--- lead_time >  8.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 93.09
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  93.09
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 26.79] class: 1
|   |   |   |   |   |   |--- no_of_special_requests >  2.50
|   |   |   |   |   |   |   |--- weights: [0.00, 55.07] class: 1
|   |   |   |   |   |--- no_of_week_nights >  9.50
|   |   |   |   |   |   |--- no_of_weekend_nights <= 4.00
|   |   |   |   |   |   |   |--- weights: [3.05, 0.00] class: 0
|   |   |   |   |   |   |--- no_of_weekend_nights >  4.00
|   |   |   |   |   |   |   |--- weights: [1.52, 0.00] class: 0
|   |   |   |--- lead_time >  90.50
|   |   |   |   |--- no_of_special_requests <= 2.50
|   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |--- lead_time <= 150.50
|   |   |   |   |   |   |   |--- arrival_year <= 2017.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 7.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 4.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.74] class: 1
|   |   |   |   |   |   |   |   |   |--- arrival_date >  4.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 26.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [7.62, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  26.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |--- arrival_month >  7.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 14.00
|   |   |   |   |   |   |   |   |   |   |--- market_segment_type_Online <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.74] class: 1
|   |   |   |   |   |   |   |   |   |   |--- market_segment_type_Online >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 6.70] class: 1
|   |   |   |   |   |   |   |   |   |--- arrival_date >  14.00
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 84.14
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [3.05, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  84.14
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.74] class: 1
|   |   |   |   |   |   |   |--- arrival_year >  2017.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 157.50
|   |   |   |   |   |   |   |   |   |--- no_of_children <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 29.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  29.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- no_of_children >  0.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 107.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 9.67] class: 1
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  107.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  157.50
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 141.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  141.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.52, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 12.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  12.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |--- lead_time >  150.50
|   |   |   |   |   |   |   |--- arrival_year <= 2017.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 1.49] class: 1
|   |   |   |   |   |   |   |--- arrival_year >  2017.50
|   |   |   |   |   |   |   |   |--- weights: [7.62, 0.00] class: 0
|   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |--- avg_price_per_room <= 153.15
|   |   |   |   |   |   |   |--- avg_price_per_room <= 73.53
|   |   |   |   |   |   |   |   |--- no_of_week_nights <= 3.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 4.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 2.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.52, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  2.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.74] class: 1
|   |   |   |   |   |   |   |   |   |--- arrival_date >  4.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 121.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 11.16] class: 1
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  121.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |--- no_of_week_nights >  3.50
|   |   |   |   |   |   |   |   |   |--- weights: [1.52, 0.00] class: 0
|   |   |   |   |   |   |   |--- avg_price_per_room >  73.53
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 90.42
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 123.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  123.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 21.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  21.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [3.05, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  90.42
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 144.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  144.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |--- avg_price_per_room >  153.15
|   |   |   |   |   |   |   |--- arrival_date <= 22.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 9.67] class: 1
|   |   |   |   |   |   |   |--- arrival_date >  22.50
|   |   |   |   |   |   |   |   |--- arrival_date <= 23.50
|   |   |   |   |   |   |   |   |   |--- weights: [1.52, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_date >  23.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 106.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.52, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- lead_time >  106.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 24.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.49] class: 1
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  24.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.98] class: 1
|   |   |   |   |--- no_of_special_requests >  2.50
|   |   |   |   |   |--- weights: [0.00, 70.70] class: 1
|--- lead_time >  151.50
|   |--- avg_price_per_room <= 100.04
|   |   |--- no_of_special_requests <= 0.50
|   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |--- market_segment_type_Online <= 0.50
|   |   |   |   |   |--- lead_time <= 163.50
|   |   |   |   |   |   |--- avg_price_per_room <= 85.50
|   |   |   |   |   |   |   |--- lead_time <= 161.50
|   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.49] class: 1
|   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.23] class: 1
|   |   |   |   |   |   |   |--- lead_time >  161.50
|   |   |   |   |   |   |   |   |--- weights: [1.52, 1.49] class: 0
|   |   |   |   |   |   |--- avg_price_per_room >  85.50
|   |   |   |   |   |   |   |--- weights: [28.95, 0.00] class: 0
|   |   |   |   |   |--- lead_time >  163.50
|   |   |   |   |   |   |--- lead_time <= 341.00
|   |   |   |   |   |   |   |--- lead_time <= 173.00
|   |   |   |   |   |   |   |   |--- arrival_date <= 3.50
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.49] class: 1
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [9.14, 48.37] class: 1
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.74] class: 1
|   |   |   |   |   |   |   |   |--- arrival_date >  3.50
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [15.24, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.23] class: 1
|   |   |   |   |   |   |   |--- lead_time >  173.00
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 98.00
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 5.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 88.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 7.44] class: 1
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  88.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [4.57, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  5.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 55.21
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  55.21
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  98.00
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.74] class: 1
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [6.10, 0.00] class: 0
|   |   |   |   |   |   |--- lead_time >  341.00
|   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 88.00
|   |   |   |   |   |   |   |   |   |--- weights: [16.76, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  88.00
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 9.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [4.57, 3.72] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  9.50
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 1 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [4.57, 2.98] class: 0
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 1 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.52, 0.74] class: 0
|   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 80.00
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.72] class: 1
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  80.00
|   |   |   |   |   |   |   |   |   |--- weights: [3.05, 2.23] class: 0
|   |   |   |   |--- market_segment_type_Online >  0.50
|   |   |   |   |   |--- avg_price_per_room <= 35.22
|   |   |   |   |   |   |--- lead_time <= 285.50
|   |   |   |   |   |   |   |--- weights: [0.00, 8.19] class: 1
|   |   |   |   |   |   |--- lead_time >  285.50
|   |   |   |   |   |   |   |--- arrival_date <= 11.00
|   |   |   |   |   |   |   |   |--- weights: [0.00, 0.74] class: 1
|   |   |   |   |   |   |   |--- arrival_date >  11.00
|   |   |   |   |   |   |   |   |--- weights: [4.57, 0.00] class: 0
|   |   |   |   |   |--- avg_price_per_room >  35.22
|   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |--- weights: [91.43, 0.00] class: 0
|   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |--- arrival_date <= 28.50
|   |   |   |   |   |   |   |   |--- weights: [9.14, 0.00] class: 0
|   |   |   |   |   |   |   |--- arrival_date >  28.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 0.74] class: 1
|   |   |   |--- no_of_adults >  1.50
|   |   |   |   |--- avg_price_per_room <= 82.47
|   |   |   |   |   |--- market_segment_type_Online <= 0.50
|   |   |   |   |   |   |--- lead_time <= 244.00
|   |   |   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 166.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.23] class: 1
|   |   |   |   |   |   |   |   |   |--- lead_time >  166.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 69.34
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.49] class: 1
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  69.34
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 19.35] class: 1
|   |   |   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 66.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 13.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 9.67] class: 1
|   |   |   |   |   |   |   |   |   |--- arrival_date >  13.00
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 62.82
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  62.82
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  66.50
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 1 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.52, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 1 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 75.75
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  75.75
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |--- lead_time >  244.00
|   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |--- arrival_year <= 2017.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 27.53] class: 1
|   |   |   |   |   |   |   |   |--- arrival_year >  2017.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 80.38
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 3.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  3.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  80.38
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 8.19] class: 1
|   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 27.53] class: 1
|   |   |   |   |   |--- market_segment_type_Online >  0.50
|   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 1 <= 0.50
|   |   |   |   |   |   |   |   |--- weights: [71.62, 0.00] class: 0
|   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 1 >  0.50
|   |   |   |   |   |   |   |   |--- weights: [141.71, 0.00] class: 0
|   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |   |   |--- arrival_date <= 3.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.49] class: 1
|   |   |   |   |   |   |   |   |--- arrival_date >  3.50
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.74] class: 1
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [7.62, 0.00] class: 0
|   |   |   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 76.87
|   |   |   |   |   |   |   |   |   |   |--- weights: [10.67, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  76.87
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 230.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.74] class: 1
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  230.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.74] class: 1
|   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |   |   |   |   |   |--- weights: [71.62, 0.00] class: 0
|   |   |   |   |--- avg_price_per_room >  82.47
|   |   |   |   |   |--- no_of_adults <= 2.50
|   |   |   |   |   |   |--- lead_time <= 324.50
|   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |   |   |   |--- market_segment_type_Corporate <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- market_segment_type_Online <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |   |   |   |--- market_segment_type_Online >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [539.43, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- market_segment_type_Corporate >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.74] class: 1
|   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |   |   |   |--- market_segment_type_Offline <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [10.67, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- market_segment_type_Offline >  0.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.74] class: 1
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 4.47] class: 1
|   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |--- market_segment_type_Offline <= 0.50
|   |   |   |   |   |   |   |   |   |--- weights: [19.81, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- market_segment_type_Offline >  0.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 85.73
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.74] class: 1
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  85.73
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 4.47] class: 1
|   |   |   |   |   |   |--- lead_time >  324.50
|   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |   |   |   |--- no_of_week_nights <= 2.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.74] class: 1
|   |   |   |   |   |   |   |   |--- no_of_week_nights >  2.50
|   |   |   |   |   |   |   |   |   |--- market_segment_type_Online <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [7.62, 0.74] class: 0
|   |   |   |   |   |   |   |   |   |--- market_segment_type_Online >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [6.10, 0.00] class: 0
|   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 <= 0.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.74] class: 1
|   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 >  0.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 5.21] class: 1
|   |   |   |   |   |--- no_of_adults >  2.50
|   |   |   |   |   |   |--- weights: [0.00, 5.21] class: 1
|   |   |--- no_of_special_requests >  0.50
|   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |--- lead_time <= 180.50
|   |   |   |   |   |--- lead_time <= 159.50
|   |   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 98.81
|   |   |   |   |   |   |   |   |--- lead_time <= 152.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.49] class: 1
|   |   |   |   |   |   |   |   |--- lead_time >  152.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 5.21] class: 1
|   |   |   |   |   |   |   |--- avg_price_per_room >  98.81
|   |   |   |   |   |   |   |   |--- weights: [1.52, 0.00] class: 0
|   |   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |   |--- arrival_date <= 12.00
|   |   |   |   |   |   |   |   |--- weights: [0.00, 0.74] class: 1
|   |   |   |   |   |   |   |--- arrival_date >  12.00
|   |   |   |   |   |   |   |   |--- arrival_date <= 23.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 157.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.52, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- lead_time >  157.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.74] class: 1
|   |   |   |   |   |   |   |   |--- arrival_date >  23.50
|   |   |   |   |   |   |   |   |   |--- weights: [6.10, 0.00] class: 0
|   |   |   |   |   |--- lead_time >  159.50
|   |   |   |   |   |   |--- arrival_date <= 1.50
|   |   |   |   |   |   |   |--- lead_time <= 176.50
|   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.49] class: 1
|   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.74] class: 1
|   |   |   |   |   |   |   |--- lead_time >  176.50
|   |   |   |   |   |   |   |   |--- weights: [3.05, 0.00] class: 0
|   |   |   |   |   |   |--- arrival_date >  1.50
|   |   |   |   |   |   |   |--- no_of_adults <= 0.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 96.08
|   |   |   |   |   |   |   |   |   |--- weights: [1.52, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  96.08
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.74] class: 1
|   |   |   |   |   |   |   |--- no_of_adults >  0.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 42.42] class: 1
|   |   |   |   |--- lead_time >  180.50
|   |   |   |   |   |--- market_segment_type_Online <= 0.50
|   |   |   |   |   |   |--- no_of_adults <= 2.50
|   |   |   |   |   |   |   |--- lead_time <= 302.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 11.91] class: 1
|   |   |   |   |   |   |   |--- lead_time >  302.50
|   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 <= 0.50
|   |   |   |   |   |   |   |   |   |--- no_of_special_requests <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.52, 1.49] class: 0
|   |   |   |   |   |   |   |   |   |--- no_of_special_requests >  1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.74] class: 1
|   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 >  0.50
|   |   |   |   |   |   |   |   |   |--- weights: [1.52, 0.00] class: 0
|   |   |   |   |   |   |--- no_of_adults >  2.50
|   |   |   |   |   |   |   |--- weights: [3.05, 0.00] class: 0
|   |   |   |   |   |--- market_segment_type_Online >  0.50
|   |   |   |   |   |   |--- no_of_special_requests <= 2.50
|   |   |   |   |   |   |   |--- no_of_week_nights <= 0.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 2.23] class: 1
|   |   |   |   |   |   |   |--- no_of_week_nights >  0.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |--- weights: [208.76, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 272.00
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 221.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [4.57, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  221.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- lead_time >  272.00
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 73.10
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.74] class: 1
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  73.10
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |--- no_of_special_requests >  2.50
|   |   |   |   |   |   |   |--- weights: [0.00, 8.93] class: 1
|   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |--- market_segment_type_Online <= 0.50
|   |   |   |   |   |--- lead_time <= 348.50
|   |   |   |   |   |   |--- no_of_week_nights <= 5.50
|   |   |   |   |   |   |   |--- arrival_date <= 30.00
|   |   |   |   |   |   |   |   |--- weights: [0.00, 112.37] class: 1
|   |   |   |   |   |   |   |--- arrival_date >  30.00
|   |   |   |   |   |   |   |   |--- no_of_week_nights <= 3.00
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.23] class: 1
|   |   |   |   |   |   |   |   |--- no_of_week_nights >  3.00
|   |   |   |   |   |   |   |   |   |--- weights: [1.52, 1.49] class: 0
|   |   |   |   |   |   |--- no_of_week_nights >  5.50
|   |   |   |   |   |   |   |--- no_of_weekend_nights <= 3.00
|   |   |   |   |   |   |   |   |--- weights: [1.52, 0.00] class: 0
|   |   |   |   |   |   |   |--- no_of_weekend_nights >  3.00
|   |   |   |   |   |   |   |   |--- weights: [0.00, 0.74] class: 1
|   |   |   |   |   |--- lead_time >  348.50
|   |   |   |   |   |   |--- avg_price_per_room <= 58.50
|   |   |   |   |   |   |   |--- weights: [0.00, 0.74] class: 1
|   |   |   |   |   |   |--- avg_price_per_room >  58.50
|   |   |   |   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |   |   |   |--- weights: [1.52, 1.49] class: 0
|   |   |   |   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |   |   |   |--- weights: [3.05, 4.47] class: 1
|   |   |   |   |--- market_segment_type_Online >  0.50
|   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |--- avg_price_per_room <= 76.48
|   |   |   |   |   |   |   |--- no_of_week_nights <= 9.00
|   |   |   |   |   |   |   |   |--- lead_time <= 216.50
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 28.28] class: 1
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.74] class: 1
|   |   |   |   |   |   |   |   |--- lead_time >  216.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 218.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.52, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- lead_time >  218.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 71.40
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  71.40
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 11.91] class: 1
|   |   |   |   |   |   |   |--- no_of_week_nights >  9.00
|   |   |   |   |   |   |   |   |--- weights: [1.52, 0.00] class: 0
|   |   |   |   |   |   |--- avg_price_per_room >  76.48
|   |   |   |   |   |   |   |--- no_of_week_nights <= 6.50
|   |   |   |   |   |   |   |   |--- arrival_date <= 27.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 233.00
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 12
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |   |   |--- lead_time >  233.00
|   |   |   |   |   |   |   |   |   |   |--- no_of_children <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |   |--- no_of_children >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [4.57, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_date >  27.50
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 234.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  234.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 269.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  269.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [4.57, 0.00] class: 0
|   |   |   |   |   |   |   |--- no_of_week_nights >  6.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 81.81
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.49] class: 1
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  81.81
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 28.00
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 204.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  204.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [9.14, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_date >  28.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.74] class: 1
|   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |--- arrival_date <= 14.50
|   |   |   |   |   |   |   |--- arrival_date <= 3.00
|   |   |   |   |   |   |   |   |--- weights: [1.52, 0.00] class: 0
|   |   |   |   |   |   |   |--- arrival_date >  3.00
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 64.43
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 8.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.23] class: 1
|   |   |   |   |   |   |   |   |   |--- arrival_date >  8.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.52, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  64.43
|   |   |   |   |   |   |   |   |   |--- required_car_parking_space <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 5.95] class: 1
|   |   |   |   |   |   |   |   |   |--- required_car_parking_space >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.74] class: 1
|   |   |   |   |   |   |--- arrival_date >  14.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 55.92
|   |   |   |   |   |   |   |   |--- weights: [0.00, 2.23] class: 1
|   |   |   |   |   |   |   |--- avg_price_per_room >  55.92
|   |   |   |   |   |   |   |   |--- no_of_special_requests <= 2.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 80.19
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.74] class: 1
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  80.19
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 83.20
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.23] class: 1
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  83.20
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |--- no_of_special_requests >  2.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.23] class: 1
|   |--- avg_price_per_room >  100.04
|   |   |--- arrival_month <= 11.50
|   |   |   |--- no_of_special_requests <= 2.50
|   |   |   |   |--- weights: [3420.94, 0.00] class: 0
|   |   |   |--- no_of_special_requests >  2.50
|   |   |   |   |--- room_type_reserved_Room_Type 6 <= 0.50
|   |   |   |   |   |--- weights: [0.00, 23.07] class: 1
|   |   |   |   |--- room_type_reserved_Room_Type 6 >  0.50
|   |   |   |   |   |--- weights: [0.00, 2.23] class: 1
|   |   |--- arrival_month >  11.50
|   |   |   |--- no_of_special_requests <= 0.50
|   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |--- weights: [0.00, 1.49] class: 1
|   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |--- weights: [0.00, 36.47] class: 1
|   |   |   |--- no_of_special_requests >  0.50
|   |   |   |   |--- arrival_date <= 24.50
|   |   |   |   |   |--- weights: [0.00, 3.72] class: 1
|   |   |   |   |--- arrival_date >  24.50
|   |   |   |   |   |--- room_type_reserved_Room_Type 1 <= 0.50
|   |   |   |   |   |   |--- lead_time <= 172.50
|   |   |   |   |   |   |   |--- no_of_special_requests <= 1.50
|   |   |   |   |   |   |   |   |--- weights: [3.05, 0.00] class: 0
|   |   |   |   |   |   |   |--- no_of_special_requests >  1.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 1.49] class: 1
|   |   |   |   |   |   |--- lead_time >  172.50
|   |   |   |   |   |   |   |--- weights: [21.33, 0.00] class: 0
|   |   |   |   |   |--- room_type_reserved_Room_Type 1 >  0.50
|   |   |   |   |   |   |--- arrival_date <= 29.50
|   |   |   |   |   |   |   |--- weights: [0.00, 2.98] class: 1
|   |   |   |   |   |   |--- arrival_date >  29.50
|   |   |   |   |   |   |   |--- weights: [1.52, 0.00] class: 0

# importance of features in the tree building ( The importance of a feature is computed as the 
#(normalized) total reduction of the criterion brought by that feature. It is also known as the Gini importance )

print (pd.DataFrame(model1.feature_importances_, columns = ["Imp"], index = X_train_1.columns).sort_values(by = 'Imp', ascending = False))

                                               Imp
lead_time                             3.546921e-01
avg_price_per_room                    1.451060e-01
market_segment_type_Online            9.927818e-02
no_of_special_requests                8.744919e-02
arrival_date                          7.923991e-02
arrival_month                         6.491302e-02
no_of_week_nights                     4.727423e-02
no_of_weekend_nights                  3.521480e-02
no_of_adults                          2.483506e-02
arrival_year                          1.478369e-02
required_car_parking_space            7.671935e-03
type_of_meal_plan_Meal Plan 1         6.398172e-03
no_of_children                        5.440778e-03
market_segment_type_Offline           4.753674e-03
type_of_meal_plan_Not Selected        4.583734e-03
room_type_reserved_Room_Type 4        4.348686e-03
room_type_reserved_Room_Type 1        3.735842e-03
type_of_meal_plan_Meal Plan 2         2.844551e-03
room_type_reserved_Room_Type 2        2.619388e-03
repeated_guest                        1.095896e-03
room_type_reserved_Room_Type 5        9.518129e-04
market_segment_type_Corporate         5.876642e-04
no_of_previous_bookings_not_canceled  5.813325e-04
market_segment_type_Aviation          5.700621e-04
room_type_reserved_Room_Type 6        5.189618e-04
room_type_reserved_Room_Type 7        3.591227e-04
market_segment_type_Complementary     1.355168e-04
room_type_reserved_Room_Type 3        1.667297e-05
no_of_previous_cancellations          3.857230e-18
type_of_meal_plan_Meal Plan 3         0.000000e+00

importances = model1.feature_importances_
indices = np.argsort(importances)

plt.figure(figsize=(10,10))
plt.title('Feature Importances')
plt.barh(range(len(indices)), importances[indices], color='violet', align='center')
plt.yticks(range(len(indices)), [feature_name[i] for i in indices])
plt.xlabel('Relative Importance')
plt.show()

Let's use pruning techniques to try and reduce overfitting.

Do we need to prune the tree?

Using GridSearch for Hyperparameter tuning of our tree model

• Hyperparameter tuning is also tricky in the sense that there is no direct way to calculate how a change in the hyperparameter value will reduce the loss of your model, so we usually resort to experimentation. i.e we'll use Grid search
• Grid search is a tuning technique that attempts to compute the optimum values of hyperparameters.
• It is an exhaustive search that is performed on a the specific parameter values of a model.
• The parameters of the estimator/model used to apply these methods are optimized by cross-validated grid-search over a parameter grid.

from sklearn.metrics import make_scorer
estimator=DecisionTreeClassifier(random_state=1)
parameter={
    "class_weight": [None, "balanced"],
    "max_depth": np.arange(2, 7, 2),
    "max_leaf_nodes": [50, 75, 150, 250],
    "min_samples_split": [10, 30, 50, 70],
}
scoring_parameter=make_scorer(recall_score)
gridObj=GridSearchCV(estimator,parameter,scoring=scoring_parameter,cv=5)
gridObj.fit(X_train_1,y_train_1)
estimator=gridObj.best_estimator_
estimator.fit(X_train_1,y_train_1)

DecisionTreeClassifier(max_depth=2, max_leaf_nodes=50, min_samples_split=10,
                       random_state=1)

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

decision_tree_perf_train_estimator= model_performance_classification_sklearn(estimator,X_train_1,y_train_1)
decision_tree_perf_train_estimator

	Accuracy	Recall	Precision	F1
0	0.759318	0.918431	0.768505	0.836806

confusion_matrix_sklearn(estimator,X_train_1,y_train_1)

GVsearch for testdata

decision_tree_perf_test_estimator= model_performance_classification_sklearn(estimator,X_test_1,y_test_1)
decision_tree_perf_test_estimator

	Accuracy	Recall	Precision	F1
0	0.763259	0.920144	0.772178	0.839692

confusion_matrix_sklearn(estimator,X_test_1,y_test_1)

• The model is giving a generalized result now since the recall scores on both the train and test data are coming to be around 0.91 which shows that the model is able to generalize well on unseen data.

Building the decision tree split

feature_name=X_train_1.columns.to_list()
feature_important=estimator.feature_importances_
tree.plot_tree(estimator,filled=True,feature_names=feature_name,impurity=True,node_ids=True,fontsize=10);

export_text for Descisiontree

print(tree.export_text(estimator,feature_names=feature_name,show_weights=True))

|--- lead_time <= 151.50
|   |--- no_of_special_requests <= 0.50
|   |   |--- weights: [3783.00, 7635.00] class: 1
|   |--- no_of_special_requests >  0.50
|   |   |--- weights: [1274.00, 9153.00] class: 1
|--- lead_time >  151.50
|   |--- avg_price_per_room <= 100.04
|   |   |--- weights: [1608.00, 1395.00] class: 0
|   |--- avg_price_per_room >  100.04
|   |   |--- weights: [2262.00, 96.00] class: 0

BAR-GRAPH for the important feature

index_sort=np.argsort(feature_important)
plt.barh(range(len(index_sort)),feature_important[index_sort]);
plt.yticks(range(len(index_sort)),[feature_name[i] for i in index_sort]);
plt.xlabel("Relative Importance")
plt.show()

Observations from the pre-pruned tree:

• Using the above extracted decision rules we can make interpretations from the decision tree model like:

• If the lead Time is less than or equal to .42 (less than half year), the no_of_special_request is less than or equal to .50,the customer mostly likly not cancel

• If the lead Time is greater than .42 (more than half year), the avg_price_per_room is less than or equal to 100.04,the customer mostly likly cancel

Decision Tree (Post pruning)

The DecisionTreeClassifier provides parameters such as min_samples_leaf and max_depth to prevent a tree from overfiting. Cost complexity pruning provides another option to control the size of a tree. In DecisionTreeClassifier, this pruning technique is parameterized by the cost complexity parameter, ccp_alpha. Greater values of ccp_alpha increase the number of nodes pruned. Here we only show the effect of ccp_alpha on regularizing the trees and how to choose a ccp_alpha based on validation scores.

Total impurity of leaves vs effective alphas of pruned tree

Minimal cost complexity pruning recursively finds the node with the "weakest link". The weakest link is characterized by an effective alpha, where the nodes with the smallest effective alpha are pruned first. To get an idea of what values of ccp_alpha could be appropriate, scikit-learn provides DecisionTreeClassifier.cost_complexity_pruning_path that returns the effective alphas and the corresponding total leaf impurities at each step of the pruning process. As alpha increases, more of the tree is pruned, which increases the total impurity of its leaves.

clf = DecisionTreeClassifier(random_state=1, class_weight="balanced")
path = clf.cost_complexity_pruning_path(X_train_1, y_train_1)
ccp_alphas, impurities = path.ccp_alphas, path.impurities

pd.DataFrame(path)

	ccp_alphas	impurities
0	0.000000e+00	0.008711
1	2.429505e-20	0.008711
2	2.429505e-20	0.008711
3	2.429505e-20	0.008711
4	2.429505e-20	0.008711
...	...	...
1924	8.757723e-03	0.328329
1925	9.901061e-03	0.338230
1926	1.279128e-02	0.351022
1927	3.400231e-02	0.419026
1928	8.097381e-02	0.500000

1929 rows × 2 columns

Graph for the ccp_alphas vs impurities

fig, ax = plt.subplots(figsize=(10,5))
ax.plot(ccp_alphas[:-1], impurities[:-1], marker='o', drawstyle="steps-post")
ax.set_xlabel("effective alpha")
ax.set_ylabel("total impurity of leaves")
ax.set_title("Total Impurity vs effective alpha for training set")
plt.show()

Next, we train a decision tree using the effective alphas. The last value in ccp_alphas is the alpha value that prunes the whole tree, leaving the tree, clfs[-1], with one node.

clfs = []
for ccp_alpha in ccp_alphas:
    clf = DecisionTreeClassifier(
        random_state=1, ccp_alpha=ccp_alpha, class_weight="balanced"
    )
    clf.fit(X_train_1, y_train_1)
    clfs.append(clf)
print(
    "Number of nodes in the last tree is: {} with ccp_alpha: {}".format(
        clfs[-1].tree_.node_count, ccp_alphas[-1]
    )
)

Number of nodes in the last tree is: 1 with ccp_alpha: 0.08097381000995474

clfs = clfs[:-1]
ccp_alphas = ccp_alphas[:-1]

node_counts = [clf.tree_.node_count for clf in clfs]
depth = [clf.tree_.max_depth for clf in clfs]
fig, ax = plt.subplots(2, 1, figsize=(10, 7))
ax[0].plot(ccp_alphas, node_counts, marker="o", drawstyle="steps-post")
ax[0].set_xlabel("alpha")
ax[0].set_ylabel("number of nodes")
ax[0].set_title("Number of nodes vs alpha")
ax[1].plot(ccp_alphas, depth, marker="o", drawstyle="steps-post")
ax[1].set_xlabel("alpha")
ax[1].set_ylabel("depth of tree")
ax[1].set_title("Depth vs alpha")
fig.tight_layout()

recall_train = []
for clf in clfs:
    pred_train = clf.predict(X_train_1)
    values_train = recall_score(y_train_1, pred_train)
    recall_train.append(values_train)

recall_test = []
for clf in clfs:
    pred_test = clf.predict(X_test_1)
    values_test = recall_score(y_test_1, pred_test)
    recall_test.append(values_test)

recall_test = []
for clf in clfs:
    pred_test = clf.predict(X_test_1)
    values_test = recall_score(y_test_1, pred_test)
    recall_test.append(values_test)  

train_scores = [clf.score(X_train_1, y_train_1) for clf in clfs]
test_scores = [clf.score(X_test_1, y_test_1) for clf in clfs]

fig, ax = plt.subplots(figsize=(15, 5))
ax.set_xlabel("alpha")
ax.set_ylabel("Recall")
ax.set_title("Recall vs alpha for training and testing sets")
ax.plot(
    ccp_alphas, recall_train, marker="o", label="train", drawstyle="steps-post",
)
ax.plot(ccp_alphas, recall_test, marker="o", label="test", drawstyle="steps-post")
ax.legend()
plt.show()

# creating the model where we get highest train and test recall
index_best_model = np.argmax(recall_test)
best_model = clfs[index_best_model]
print(best_model)

DecisionTreeClassifier(ccp_alpha=0.03400231175331703, class_weight='balanced',
                       random_state=1)

confusion_matrix_sklearn(best_model,X_train_1,y_train_1)

decision_tree_post_perf_train = model_performance_classification_sklearn(
    best_model, X_train_1, y_train_1
)
decision_tree_post_perf_train

	Accuracy	Recall	Precision	F1
0	0.759318	0.918431	0.768505	0.836806

confusion_matrix_sklearn(best_model,X_test_1,y_test_1)

decision_tree_post_test = model_performance_classification_sklearn(
    best_model, X_test_1, y_test_1
)
decision_tree_post_test

	Accuracy	Recall	Precision	F1
0	0.763259	0.920144	0.772178	0.839692

plt.figure(figsize=(20, 10))

out = tree.plot_tree(
    best_model,
    feature_names=feature_name,
    filled=True,
    fontsize=9,
    node_ids=False,
    class_names=None,
)
for o in out:
    arrow = o.arrow_patch
    if arrow is not None:
        arrow.set_edgecolor("black")
        arrow.set_linewidth(1)
plt.show()

# importance of features in the tree building ( The importance of a feature is computed as the 
#(normalized) total reduction of the 'criterion' brought by that feature. It is also known as the Gini importance )

print (pd.DataFrame(best_model.feature_importances_, columns = ["Imp"], index = X_train_1.columns).sort_values(by = 'Imp', ascending = False))

#Here we will see that importance of features has increased

                                      Imp
lead_time                             1.0
no_of_adults                          0.0
type_of_meal_plan_Meal Plan 3         0.0
market_segment_type_Offline           0.0
market_segment_type_Corporate         0.0
market_segment_type_Complementary     0.0
market_segment_type_Aviation          0.0
room_type_reserved_Room_Type 7        0.0
room_type_reserved_Room_Type 6        0.0
room_type_reserved_Room_Type 5        0.0
room_type_reserved_Room_Type 4        0.0
room_type_reserved_Room_Type 3        0.0
room_type_reserved_Room_Type 2        0.0
room_type_reserved_Room_Type 1        0.0
type_of_meal_plan_Not Selected        0.0
type_of_meal_plan_Meal Plan 2         0.0
no_of_children                        0.0
type_of_meal_plan_Meal Plan 1         0.0
no_of_special_requests                0.0
avg_price_per_room                    0.0
no_of_previous_bookings_not_canceled  0.0
no_of_previous_cancellations          0.0
repeated_guest                        0.0
arrival_date                          0.0
arrival_month                         0.0
arrival_year                          0.0
required_car_parking_space            0.0
no_of_week_nights                     0.0
no_of_weekend_nights                  0.0
market_segment_type_Online            0.0

print(tree.export_text(best_model, feature_names=feature_name, show_weights=True))

|--- lead_time <= 151.50
|   |--- weights: [7705.88, 12493.42] class: 1
|--- lead_time >  151.50
|   |--- weights: [5897.12, 1109.58] class: 0

• We can see that the observation we got from the pre-pruned tree is also matching with the decision tree rules of the post pruned tree.
• If the lead_time is less than or equal to 151.50 is less likely to cancel.

importances = best_model.feature_importances_
indices = np.argsort(importances)
plt.figure(figsize=(12, 12))
plt.title("Feature Importances")
plt.barh(range(len(indices)), importances[indices], color="violet", align="center")
plt.yticks(range(len(indices)), [feature_name[i] for i in indices])
plt.xlabel("Relative Importance")
plt.show()

Model Performance Comparison and Conclusions

# training performance comparison

models_train_comp_df = pd.concat(
    [
        decision_tree_perf_train_without.T,
        decision_tree_perf_train_with_weigth.T,
        decision_tree_perf_train_estimator.T,
        decision_tree_post_perf_train.T,
    ],
    axis=1,
)
models_train_comp_df.columns = [
    "Decision Tree without class_weight",
    "Decision Tree with class_weight",
    "Decision Tree (Pre-Pruning)",
    "Decision Tree (Post-Pruning)",
]
print("Training performance comparison:")
models_train_comp_df

Training performance comparison:

	Decision Tree without class_weight	Decision Tree with class_weight	Decision Tree (Pre-Pruning)	Decision Tree (Post-Pruning)
Accuracy	0.993972	0.992832	0.759318	0.759318
Recall	0.995569	0.991794	0.918431	0.918431
Precision	0.995460	0.997524	0.768505	0.768505
F1	0.995514	0.994651	0.836806	0.836806

models_test_comp_df = pd.concat(
    [
        decision_tree_perf_test_without.T,
        decision_tree_perf_test_with_weigth.T,
        decision_tree_perf_test_estimator.T,
        decision_tree_post_test.T,
    ],
    axis=1,
)
models_test_comp_df.columns = [
    "Decision Tree without class_weight",
    "Decision Tree with class_weight",
    "Decision Tree (Pre-Pruning)",
    "Decision Tree (Post-Pruning)",
]
print("Test set performance comparison:")
models_test_comp_df

Test set performance comparison:

	Decision Tree without class_weight	Decision Tree with class_weight	Decision Tree (Pre-Pruning)	Decision Tree (Post-Pruning)
Accuracy	0.870438	0.862940	0.763259	0.763259
Recall	0.898544	0.890362	0.920144	0.920144
Precision	0.908204	0.904722	0.772178	0.772178
F1	0.903348	0.897485	0.839692	0.839692

• Decision tree models with pre-pruning and post-pruning both are giving equally high recall scores on both training and test sets.
• However, we will choose the post pruned tree as the best model since it is giving a slightly high precision score on the train and test sets than the pre-pruned tree.

Actionable Insights and Recommendations

Profitability and Cancellation Policies

• Lead Time Management: Bookings with longer lead times (> 6 months) are more prone to cancellations. Implement a 24-hour cancellation policy for bookings made more than 6 months in advance. This allows the hotel to rebook the room if a cancellation occurs, minimizing revenue loss.

• Handling Special Requests: Guests with specific requests tend to have lower cancellation rates if their needs are met. Maintain a record of frequent special requests and proactively offer these amenities. This could improve customer satisfaction and reduce cancellations.

• Promotion Strategies: Promotions like "0 dollar offers" may lead to increased cancellations. Transition from heavy discounts to value-added promotions, such as bundled services or loyalty points. This strategy can attract more committed customers and enhance revenue.

• Booking Horizon Management: Displaying booking options too far in advance may lead to higher cancellation rates. Limit the booking horizon to 8-12 months. This approach balances availability with the risk of cancellations.

• Room Type Optimization: Certain room types (e.g., complimentary rooms) might contribute to higher cancellation rates. Increase the availability of corporate rooms and re-evaluate the allocation of complimentary rooms to reduce cancellations and enhance profitability.

• Segment-Specific Policies: Different market segments may exhibit varied cancellation behaviors. Customize cancellation policies for each market segment. For instance, corporate clients might benefit from flexible policies, whereas leisure travelers might be offered stricter terms.

Additional Recommendations

• Detailed Room Data: More granular data on room types can help identify which rooms are underbooked. Collect detailed information on room preferences and booking patterns to tailor marketing and pricing strategies. This can help in promoting underbooked rooms and optimizing occupancy rates.

• Customer Feedback Loop: Understanding customer preferences and feedback can enhance service offerings. Implement feedback mechanisms to gather insights on guest preferences and adjust services accordingly. This can lead to improved guest satisfaction and reduced cancellations.

• Data-Driven Decision Making: Continuous analysis of booking and cancellation data is essential for dynamic strategy adjustments. Establish a data analytics team to monitor booking trends and customer behavior. This team can provide actionable insights to optimize pricing, promotions, and operational strategies.